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9 Tips On How To Prioritize Tasks Effectively At Work

There’s nothing more satisfying than checking off work on your to-do list. But before you attack your task list, you want to learn how to prioritize tasks. 

Prioritizing tasks helps you:

• Meet deadlines by getting the most critical work done first.
• Better utilize scarce time and team resources.
• Effectively manage your team’s workload.

Project management tools help you capture, prioritize, and organize your work. You can also use simple task management tools — from simple to-do lists to visual Kanban boards — to get things done. But, irrespective of the tool, you need to learn how to prioritize work.

In this article, you’ll learn about prioritization methods. Here’s what we’ll cover:

• Make A Master Task List
• Categorize Tasks Using The 4Ds Of Time Management
• Prioritizing Tasks Using Eisenhower Power Matrix
• Prioritize Project Tasks Using The MoSCoW Method
• Prioritize Using Relative Priorities
• Focus On The Most Important Tasks Of The Day
• Do The Most Difficult Task First
• Prioritize Using The Pareto Principle
• Review & Revise Task Priorities

Ready to get started, let’s begin.

1. Make A Master Task List

Task requests come from various sources — your boss, different team members, colleagues, partners, and clients. Task requests can also come from multiple channels — emails, slack messages, or even a watercooler talk.

You need a way to capture all these requests and identify top priorities. 

In addition, a task list also helps keep track of project progress. Plus, taking up the task request then, can mess up the rest of your schedule.

This is why you need a way to capture all your work in one place. It could be a notebook, a to-do list app on your phone, or the task backlog in your project management tool .

Once you have a list of all your tasks ready, you can easily categorize and prioritize them.

2. Categorize Tasks Using The 4Ds Of Time Management

Before you can prioritize your task items, you need to categorize each task in your master list into one of these four categories:

• Do the task now
• Defer the task to a later time
• Delegate the task to someone else
• Delete the task from your list

The “Do, Defer, Delegate, Delete” framework is also called the 4Ds of time management . 

You can do a few tasks on your task list quickly. They take less than a minute or two. And there’s no dependency on anyone else. Go ahead and do these to trim down your to-do list.

A few other tasks can be done by your subordinates or team members. They have the skills and the necessary information to complete the work. Delegate this work to trim down your task list further.

You’ll also find some tasks that you don’t need to do anymore. They have been on your task list forever. Or, compared to the estimated effort involved, they provide very little value. Delete these from your list.

The only work that you’re left with now is the deferred tasks. You cannot do, delegate, or delete these. This is the work that needs prioritizing. 

Next, let’s look at three ways to organize your tasks by priority.

3. Prioritizing Tasks Using Eisenhower Power Matrix

The Eisenhower four-quadrant, power matrix is a straightforward framework for prioritizing work. 

Dwight Eisenhower — the 34th president of the United States — conceptualized the power matrix. But it was Steven Covey who made it popular in his best-seller — The 7 Habits Of Highly Effective People”.

The power matrix framework helps you answer two questions. Which tasks are important? And which tasks can be eliminated altogether?

To prioritize work using this framework, put each task into one of the four quadrants:

• Urgent and important
• Important, but not urgent
• Urgent, but not important
• Neither urgent nor important

Urgent and important tasks have the highest priority. Not doing these asap will have negative consequences. 

Important but not urgent tasks will take up most of your time. Avoid neglecting these until the last minute to prevent unbalanced schedules and workloads.

Urgent but not important tasks can be delegated to your team. You don’t have to do them yourself.

Finally, you can eliminate tasks that are neither urgent nor important.

4. Prioritize Project Tasks Using The MoSCoW Method

Budget and time constraints are big challenges in project management. These challenges often directly affect the project’s outcomes.

The MoSCoW method helps prioritize work based on outcomes. Thus, it’s most effective when managing projects. 

It was developed by Dai Clegg, a software engineer, during his tenure at Oracle. 

This method helps stakeholders and clients understand the significance of each outcome in the final project delivery.

To use MoSCoW prioritization, divide your project outcomes into one of the four buckets:

• Must have: Outcomes without which the entire project is considered a failure
• Should have: Outcomes that are not as critical as must-have outcomes but still important
• Could have: Outcomes that can be delivered when you have budget or time remaining for final project delivery
• Won’t have: Outcomes that won’t be delivered with the project

Once you’ve categorized the outcomes above, you can prioritize work that supports these outcomes.

5. Order Tasks Using Relative Priorities

Using the prioritization strategies above often helps. But how do you order tasks with similar priority? What if you have lots of tasks that are important but not urgent? Or tasks that support must-have outcomes? 

How do you prioritize items that have the same priorities? What prioritization methods do you use?

The answer is simple — using relative priority. Relative priority works by weighing the importance of each task compared to other tasks on the priority list. Then ordering the tasks based on this weight. 

For example, let’s say you have ten work items. Assign a number from 1-10 to each task. And in no time, you’ll have sorted high and low priority tasks.

But, how do you decide which task is more important?

Two straightforward ways are to order work by their due dates and by dependencies. Tasks with an earlier deadline become priority tasks. Also, complete tasks that block other work from starting first.

Next, let’s look at three techniques that’ll help you breeze through your personal, prioritized task list.

6. Focus On The Most Important Tasks Of The Day

Leo Babuta of Zen Habits popularized the Most Important Tasks (MITs) method. This prioritization technique can help if you struggle to get daily tasks done even after prioritizing them.

To use the MITs method, choose up to three tasks that you want to get done today. These are your most important tasks. Complete these first. 

Then if time permits, you can take up other miscellaneous work. This extra work is a bonus. You only work on bonus tasks if you finish the MITs.

You can consider your day successful even if you just do your MITs.

7. Do The Most Unwanted Task First

But what if you struggle to get through your MITs?

Thanks to Bryan Tracy’s method and his book — Eat That Frog! — you’ll have an answer in the following few paragraphs.

But what do frogs have in common with Task prioritization?

Mark Twain has the answer:

If it’s your job to eat a frog, it’s best to do it first thing in the morning. And if it’s your job to eat two frogs, it’s best to eat the biggest one first.

Once you’ve eaten the frog, there’s nothing worse you’ll have to tackle for the rest of the day. Replace frogs with tasks, and you’ll have your answer.

Most people put off complex tasks. And not because they’re less important. But only because they seem difficult. Bryan Tracy recommends you get unwanted work out of your way first. Then getting through the rest of the work becomes a breeze.

8. Prioritize Tasks Using The Pareto Principle

If you hate frogs like most people, the Pareto Principle is another popular way to blast through your daily task list. It’s also known as the 80/20 rule or the “law of the vital few.”

According to this principle, 80% of your day’s success depends on 20% of the tasks. 

It’s tricky to identify the 20% work. The best way is to look at your MITs and pick one task you feel will make your day a success. For example, it could be an important task that provides the most value or takes up the most time. 

Once you’ve identified the task, stay focused on getting that one task done.

Together the Pareto Principle and Bryan Tracy’s methods will help you blast through your daily task list.

9. Review & Revise Task Priorities

Do you know what the most frustrating thing about managing work is?

You’re halfway through your work plan, and suddenly tasks and/or priorities change. There’s little you can do with this change. It’s a risk that you have to live with. 

Because of these changes, it’s critical to review and revise project task priorities often. 

If budget, resource, or time constraints change, revise project priorities using the 4Ds framework, Eisenhower’s Power Matrix, or the MoSCoW method.

And if short-term priorities change, review and revise your relative task priorities.

Armed with the tips in this article, you’re now ready to tackle work and bring clarity to your team.

Speaking of clarity, you may want to check out Toggl Plan . It’s a beautifully simple work management tool that provides clear visual cues about what needs to be done, by whom, and when. For project managers, Toggl Plan comes with easy, drag-and-drop timelines that make planning work a breeze.

Start your free Toggl Plan team trial now .

Jitesh is an SEO and content specialist. He manages content projects at Toggl and loves sharing actionable tips to deliver projects profitably.

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Eisenhower Matrix: How to prioritize tasks (examples, template)

In this guide, we’ll introduce you to the Eisenhower Decision Matrix and show you how to use it to prioritize your team’s time and tasks.

We’ll show you some Eisenhower Matrix examples and provide a free, customizable template you can use when implementing the approach on your team.

What is the Eisenhower Matrix?

The Eisenhower Matrix, also known as the Eisenhower Decision Matrix, Eisenhower Box, and Urgent-Important Matrix, is a time and task management tool that helps individuals prioritize their tasks by considering two factors: urgency and importance.

It is named after Dwight D. Eisenhower, the 34th President of the U.S., who was known for his effective time management strategies.

To use the Eisenhower Matrix, first identify all the tasks you need to complete. Then, based on the urgency and importance of each one, place it in one of the four quadrants of the matrix:

Here’s what each quadrant of the Eisenhower Matrix represents:

• Urgent and important — Tasks that you need to complete as soon as possible. These are your top priority
• Not urgent but important — Tasks that are critical but don’t have a pressing deadline. You should schedule time for them, otherwise, they might become urgent
• Urgent but not important — Tasks that are pressing but not important. If possible, you should delegate them
• Not urgent and not important — Tasks that are neither urgent nor important. You should eliminate them if possible

Who uses the Eisenhower Matrix?

Anyone who needs to manage their work can use this approach. It is particularly useful for project managers because it helps them focus on the most important tasks and avoid being overwhelmed by urgent but unimportant tasks.

If you’re a product manager or a project manager, you might use the Eisenhower Matrix to do things such as:

• Identify and prioritize project tasks — Determine what tasks need to be completed for a and place them in the appropriate quadrant based on their level of urgency and importance. This will help you focus on the most important tasks first
• Delegate tasks — Identify tasks that are urgent but not important and delegate them to team members or other resources if possible. This will free up your time to focus on more important tasks
• Manage project resources — Identify tasks that are important but not urgent and schedule them in advance to ensure that they are completed on time. This will help you manage your resources effectively and avoid last-minute rushes to complete tasks
• Monitor progress — Track your progress on tasks and identify any bottlenecks or roadblocks. This will help you stay on track and ensure that your project stays on schedule

By using the Eisenhower Matrix, you can effectively manage your time and resources and ensure that projects are completed on time and within budget.

Eisenhower Matrix example

Let’s see an example of how the Eisenhower Matrix works in practice.

In this example, the project manager has identified the following tasks:

• Write project proposal (quadrant 1)
• Develop project plan (quadrant 1)
• Conduct market research (quadrant 2)
• Attend team meeting (quadrant 3)
• Write a report on competitor analysis (quadrant 4)

Based on the level of urgency and importance of each task, the project manager has placed them in the appropriate quadrant of the matrix:

Here’s how you can read the example Eisenhower Matrix above:

• The project manager should focus on the tasks in quadrant 1 first because they are both urgent and important
• The task in quadrant 2 also warrants attention because it is important but not urgent
• The task in quadrant 3 can be delegated or postponed because it is urgent but not important
• The task in quadrant 4 can be eliminated, as it is neither urgent nor important

Eisenhower Matrix template

If you want to give the Eisenhower Decision Matrix a try, you can use our free, customizable template to get started.

To use the Eisenhower Matrix template , click here and then select File > Make a copy from the menu above the spreadsheet.

How to categorize your tasks

Categorizing tasks using the Eisenhower Matrix can be a helpful way to prioritize your tasks and manage your time effectively. To realistically categorize your tasks, it is important to be honest with yourself about the level of importance and urgency of each task.

Before you begin categorizing your tasks, take some time to think about what is important to you and your goals. This will help you prioritize tasks that align with your values and objectives.

Think about the potential consequences of not completing a task. If the task is important, there will likely be negative consequences if it is not completed. On the other hand, if the task is not important, the consequences of not completing it may be minimal.

Establish deadlines for each task to help determine its level of urgency. A task with an imminent deadline is likely to be more urgent than a task with a longer timeline.

It is important to be consistent in your categorization of tasks. If you consistently categorize tasks based on their level of importance and urgency, you will be better able to prioritize your time and efforts.

From time to time, review and update your categorization of tasks, as priorities can change over time. Make sure to reassess the level of importance and urgency of your tasks regularly to ensure that you are focusing on the most important tasks.

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How to get your team using the Eisenhower Matrix

If you plan to use the Eisenhower Matrix, you first need to explain the concept of the Eisenhower Matrix to your team and how it can be used to prioritize work and manage time efficiently.

Have each team member identify all the tasks they need to complete, including both work-related and personal tasks.

Then, have team members categorize their tasks using the matrix, placing each task in the appropriate quadrant based on its level of importance and urgency.

Encourage team members to focus on tasks in quadrant 1 first because they are both urgent and important. They should also give attention to tasks in quadrant 2, which are important but not urgent. Tasks in quadrant 3 can be delegated or postponed, while tasks in quadrant 4 should be eliminated if possible.

Encourage team members to regularly review and update their task priorities. This will help them stay on track and ensure that they are focusing on the most important tasks.

Limitations

Like any other framework or tool, the Eisenhower Matrix is not a one-size-fits-all solution and has some limitations.

For one, categorization of tasks into the quadrants of the matrix can be subjective because the level of importance and urgency of tasks can vary depending on the individual and the context. This can make it difficult to determine the appropriate categorization of certain tasks.

Furthermore, the Eisenhower Matrix does not consider external factors such as the workload or the availability of resources. It also does not account for unexpected events or priorities that may change during the day.

Alternative prioritization and task management frameworks

There are many different frameworks and tools that can be used to prioritize tasks and manage time effectively. Here is how the Eisenhower Matrix compares with other popular prioritization frameworks:

Priority Matrix

Time management matrix, pareto principle (80/20 rule).

The Priority Matrix is a tool that helps individuals prioritize tasks based on their potential impact and the resources required to complete them. It consists of a matrix with four quadrants, similar to the Eisenhower Matrix. However, the criteria used to categorize tasks are different.

The Time Management Matrix , also known as the Covey Matrix, is a tool developed by Stephen Covey to help individuals prioritize tasks based on their level of importance and urgency. It consists of four quadrants similar to the Eisenhower Matrix, but the criteria used to categorize tasks are slightly different.

The Pareto Principle, also known as the 80/20 rule , states that 80 percent of the effects come from 20 percent of the causes.

This principle can be used to prioritize tasks by focusing on the 20 percent of tasks that are most important and will have the greatest impact.

Kanban is a project management method that uses a visual board to track the progress of tasks through various stages of completion. It is a flexible tool that can be used to prioritize tasks by moving them to the top of the board or assigning them to specific team members.

The Eisenhower Matrix can be a valuable tool for managing time and priorities efficiently and achieving your goals, whether you are an individual or part of a team.

By understanding the framework and how to implement it, you can take control of your time and be more productive.

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How to Prioritize Tasks: Steps, Tips, and Techniques

By Kate Eby | August 26, 2021 (updated September 19, 2023)

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Task prioritization enables you to more effectively manage work tasks by identifying priority levels, based on urgency and importance. This article provides expert tips and best practices to help you prioritize your workload.

Included on this page, you’ll find expert tips and tricks for prioritizing work tasks, a variety of prioritization techniques , and a free priority matrix template that you can download to get started.

What Is Task Prioritization?

Task prioritization is the process of assigning order to tasks based on their urgency and importance. The goal is to improve your time management and productivity by focusing on and organizing the tasks you need to complete. 

Prioritizing tasks can often mean putting aside a task you want to do, and instead taking care of  harder or more challenging responsibility. Task prioritization is a process, and you can use several steps, techniques, and tips to help you effectively focus your attention and manage your time.

General Steps to Prioritizing Tasks

You can prioritize tasks by recognizing a number of factors involved with a single task. By understanding the priority of each task, you can then assign an order to determine when and how to accomplish each task.

• Make a List: Document the current outstanding tasks. Jot down any tasks or assignments that come to mind so that you can visualize the extent of the work awaiting you.
• Know the Deadlines: Add task deadlines to the list. Doing so allows you to more effectively separate your urgent tasks from less time-sensitive items. 
• Estimate the Time and Effort: By understanding the resources it takes to complete each task, you can more effectively identify the priority of a task and plan a course of action for tackling your list.
• Determine the Highest-Priority Tasks: Base this ranking on the tasks’ deadlines and estimated time to completion. Identifying the highest-priority tasks, based on urgency and importance, will give you clarity on what needs the most attention.
• Complete Highest-Priority Tasks First: Sometimes the hardest task is the one with the highest priority. By completing your highest-priority tasks over those you would prefer doing, you can increase efficiency in managing your priorities and time.
• Remain Flexible: The needs of your tasks may change when you least expect them — a deadline may be extended or shortened, or an assignment may suddenly be cut. It is important to remain flexible so that you can adjust task priorities and tackle your to-do list the most effectively.
• Review and Reevaluate Tasks Often: Keep track of your tasks and deadlines. Staying on top of your to-do list will help you complete priorities, ensure you don’t overlook important tasks, and remove tasks that are no longer urgent or important.

How to Prioritize Tasks at Work

In order to prioritize tasks at work, be aware of what you need to do and when you need to do it. You might find it difficult to execute on high-priority tasks when you prefer to work on other tasks. 

Phoebe Gavin , an author and productivity expert who helps people balance their lives through career, life, and productivity coaching, recommends the following:

• Set Deadlines for Every Task: Regardless of the size of the task, set a deadline. If a work item doesn’t already have a firm deadline, you may have to approach your colleagues for input. For example, a colleague might email you and ask you to review a presentation when you have time. Because there is no specified deadline, you may need to collaborate with your colleague to take into account the presentation’s due date and any other outstanding tasks on your list.
• Give Yourself Five Minutes of Willpower: Getting started is one of the greatest challenges to staying productive and focused. Starting on a challenging (or personally uninteresting) task is often daunting and can lead to procrastination. By assigning yourself five minutes to focus on the task, you will likely find that that five minutes will turn into more time, and you will make more progress and maybe complete the task altogether. For example, you might dread a lengthy assignment, and the due date is quickly approaching. Look at a blank document and decide, “I’m just going to work on this for five more minutes.” After focusing your attention for five minutes, you will likely find that you have already created a plan for your assignment and feel ready to complete more.
• Focus on One Task at a Time: Avoid multitasking, as this is not as effective or as productive as focusing on a single task. While multitasking might seem an effective use of time, you may ultimately waste your time by performing poorly on multiple tasks. Imagine that you are in a meeting, and you recognize an opportunity to respond to outstanding emails. As you write your emails, conversation in the meeting becomes indistinct background noise. Because of this, you miss a direct question from a colleague and have to be reminded to answer. You also realize, after the meeting, that one of the emails you sent was addressed to the wrong person and contained several errors that you have to fix and resend.
• Connect with Your “Why”: To prioritize your tasks, it is important to know your “why,” whether it’s the reason you hold a particular job, the goals you would like to achieve, or your personal values. The more you can connect with the tasks on your list and how they fit with your purpose and identity, the easier it will be to understand which of your tasks should rank higher on your to-do list. For example, imagine that you have started a new job and want to be seen as diligent and trustworthy among your boss and coworkers. You have a number of tasks to complete, including one you promised you would wrap up for a coworker. You choose to finish this task first, as doing so will fulfill your personal value of wanting to be seen as diligent and trustworthy.

Techniques for Task Prioritization

You can use many techniques for prioritizing tasks, such as creating a to-do list and limiting distractions, that can improve productivity and performance. The following techniques can help guide you through successfully organizing and completing your outstanding tasks.

Create a To-Do List

Create a to-do list that identifies and provides an overview of all your outstanding tasks. This will help you keep track of and visualize the tasks you need to do. Making a daily to-do list can be crucial for staying focused and meeting deadlines.

Example: You have what seems like a never-ending list of tasks floating around in your head. This list seems impossible to tackle, and you have to draw on your memory to recall the different requests and assignments. Organize your thoughts and jot down each task. By making a list, you realize there is an end to your tasks and can visualize your progress.

Check out one of these free task list templates to get a head start on your to-do list.

Eisenhower Priority Matrix

The Eisenhower Priority Matrix is a method that helps you choose which tasks should come first, based on urgency and importance. The matrix consists of four quadrants that are designed to put the priority level of a task into perspective. 

Example: You have a huge list of tasks to complete today and feel overwhelmed. Put each task through the Eisenhower Priority Matrix to identify the most important and urgent tasks. In doing so, you realize which tasks you need to address and plan out, and which tasks you need to assign to someone else. You may also find that you can completely remove some tasks.

Check out some of our priority matrix templates .

ABCDE Method

The ABCDE method assigns a letter value to each task: An A rating is the highest priority and needs to be addressed first, and an E rating is the lowest priority and can be addressed later.

Example: It’s Monday morning and you have a list of tasks that need your attention. One of those tasks is to create a short document, which you have continually pushed off for other assignments. But the deadline is approaching, and you have been asked to conduct a meeting, which will take a lot of preparation. The meeting will take place Friday, while the short document is due Tuesday. In using the ABCDE method, you assign a value of A to the short document, and a B to the meeting preparation, as it can be addressed later.

The chunking technique utilizes your time and productivity by identifying the similarities between the tasks on your to-do list. By chunking together similar tasks, you can get more work done quicker and more effectively, rather than by multitasking. This is a more focused and managed approach to completing the tasks on your to-do list.

Example: At work, you have a series of tasks you need to complete, and you receive additional requests from your coworkers and manager. Use the chunking method to write down every request and task. Review your list and identify similarities among tasks (e.g., those that involve client communication, such as messaging a client, calling another client, and setting up a meeting with a client). By chunking these tasks together, you are more focused and in the mindset of communicating with clients, and this makes completing the tasks quicker and easier.

1-3-5 Method

Using the 1-3-5 method , you reimagine your daily to-do list to help you take on tasks with clarity and focus. Start by jotting down nine tasks to complete for the day. Of these nine tasks, one should be a big task, three should be medium-sized tasks, and five should be small tasks. By organizing your to-do list using the 1-3-5 method, you can more effectively manage your time at work and meet the needs of your tasks.

Example: Lately, you feel disorganized. The tasks you complete seem unbalanced, and you feel like you are working on too many larger projects, while neglecting the smaller, equally important tasks. Using the 1-3-5 method, reprioritize your workday by deciding to limit your time on the larger projects to ensure you address tasks of all sizes. Rather than spending the whole day working on larger projects, set a schedule for the day that involves completing one larger project, three medium-sized tasks, and five smaller tasks. This method will help you feel less overwhelmed and more productive.

Getting Things Done (GTD) Method

The Getting Things Done (GTD) method , developed by productivity consultant David Allen, involves the following five pillars: capture, clarify, organize, reflect, and engage. Capture is the process of decluttering the tasks in your mind by writing them on a to-do list. Then, you clarify your tasks by breaking down larger tasks and discarding unimportant tasks. Then, organize the tasks on your list: Categorize and chunk tasks based on similarities and urgency. As you work, reflect on your lists frequently to update and revise the items. Lastly, engage directly with the tasks to complete them.

Example: You start a new role at work and are stressed by the different items you need to complete, which include onboarding tasks, studying all the areas of your new department, and meeting with several people for guidance. Use the GTD method to make headway on your to-do list. Start by writing down each task, and then breaking down some of the larger tasks into smaller, more manageable items. Following this, categorize the tasks on your list based on similarities. Because the role you are in is fast-paced and constantly changing, you will need to revise your list frequently to make sure your tasks reflect your needs. With a clear plan for your work, you are able to focus better on your tasks and complete them.

Eat the Frog

Eat the Frog refers to completing the most difficult and important task before all other work. This is most likely the task you dread most and often put off. By finishing your most daunting, highest-priority task first, you will likely improve your productivity.

Example: It is the beginning of the day and your colleague sends you a reminder to review a draft. You dread reading through the lengthy draft and would prefer to complete your smaller tasks. But you decide to tackle the review. After you complete the review, you are relieved (as is your colleague) and can continue the rest of your day with smaller tasks.

Know Your Distractions

Two main types of distractions can hinder your productivity. Environmental distractions are those that we can most easily control and can overcome with such simple actions as hiding your phone from your work area or decluttering your desk. Procrastination distractions occur when an environmental distraction becomes more prominent and serves as an unhealthy coping mechanism to dealing with stress. Procrastination prevents us from completing our work in an effective manner and can lead to more stress.

By first knowing — and limiting — your environmental distractions, you can prevent procrastination.

Example: You are trying to finish a report when your phone buzzes. Your phone sits right beside your computer, and you can see each notification. You stop working on your report to check a news notification, and suddenly find yourself scrolling through your phone for an extended period. Even when you set your phone down, you feel distracted from your report and can’t concentrate. Instead of continuing to scroll through posts, turn off your phone and place it out of sight. This allows you to concentrate on your work without distractions.

Warren Buffett’s Two-List Strategy

Warren Buffett’s two-list strategy involves jotting down 25 of the most important goals currently on your mind. Out of the 25 goals, circle five that you believe are the most critical in your life. These goals could cover your life, your career, or other areas. 

Using this list, make two lists:

• Most Important Goals: Highlights the goals most valuable to your life 
• Avoid At All Costs: Identify the goals that do not help you to accomplish your long-term priorities

Example: You have always had a long list of things you would like to accomplish in your life, many of which will take years of work. You want to go back to school to finish your MBA, work as a business professor, and move up in your current job to become a marketing director, but have also thought about a career in the medical field. At the same time, you also want to start a family and be involved as much as possible, among many other personal goals. Using Warren Buffett’s two-list strategy, you write out 25 of your major life goals. After determining your 25, you circle the five that most closely align with your larger goals. You circle getting your MBA, rising to a marketing director position, starting a family, saving for a good retirement, and working a four-day week to spend more time with family.  By doing this, you decide to spend less time, if any time at all, on the goals on the Avoid at All Costs list. This will free up time to help you move toward your five most important goals.

Ivy Lee Method

The Ivy Lee method requires you to prioritize your day by following a set of rules, which were developed by productivity consultant Ivy Lee more than 100 years ago. The rules of the Ivy Lee method are as follows:

• End every workday by writing down the six most important tasks you need to complete the next day.
• Order the six tasks by importance to ensure the highest-priority tasks are at the start of your to-do list for tomorrow.
• The next day, complete each of the tasks in order, if possible.
• Stay focused on each task, and avoid moving onto a different task before finishing the task at hand.
• At the end of the workday, develop a new list of six tasks to complete the next day. If you don’t complete a task, make sure to add it to the list for the next day.
• Repeat the process.

Example: You feel overwhelmed by the number of assignments and tasks at work. You need to complete a slide deck ASAP, prepare for a meeting with a customer that will take place in a few days, and research and gather participants for an upcoming conference, all while also writing several descriptions for new training content. You need to reevaluate your tasks and make sure you complete your most important priorities.

You try the Ivy Lee method and write out six tasks that you believe are most important and plan to complete tomorrow at work. You order the tasks based on their priority level. You assign completing the slide deck as your number one priority, researching participants as number two, reaching out to the participants as number three, and preparing for your meeting with the customer as number four. For the last two spots, you plan to focus on writing out two of the several descriptions you need to write. The next day at work, you follow each task, one by one, making sure not to multitask or move between tasks. At the end of the day, you find that you finished five of the six tasks and feel far more accomplished than you had the previous day. You create a new list of six tasks for the next day, making sure to include the description assignment you were not able to complete.

Expert Tips and Tricks for Task Prioritization

You can use many tips and tricks to help prioritize your tasks. Make the most of your time and accomplish the tasks on your list by implementing some of the following tips and tricks, as proposed by Phoebe Gavin:

• Promote Emotional Awareness: Emotions can sometimes be a barrier to completing what needs to be done. Recognize your emotions — both how you feel generally and how you feel toward the task at hand — to avoid coping mechanisms that lead to procrastination. Greater emotional awareness can help you create stronger and healthier coping mechanisms that help you focus on your tasks, even the most difficult ones.
• Break Down Large Tasks: Divide tasks into smaller, more manageable tasks in order to make them more approachable. Doing so also highlights progress on the larger task at hand. Learn more about managing large workloads by reading our comprehensive guide.
• Limit Environmental Distractions: Outside distractions can make completing a task feel impossible. Avoid distractions such as your phone, IM chat windows, email, and whatever else you might find yourself reaching for instead of the task at hand. By removing your environmental distractions, you can improve your focus on high-priority tasks.

Other useful expert tips and tricks include the following: 

• Use the Pareto Principle: The Pareto principle proposes that 80 percent of your results stem from 20 percent of your efforts. By keeping this principle in mind, you can more effectively prioritize your tasks to fit your goals and results.
• Adopt a Scheduling Tool: Doing so will help you keep track of your tasks and due dates to ensure that you don’t overlook any urgent or important tasks. Keeping your tasks organized will help you stay productive and prioritize your time. 
• Delegate Tasks: If your to-do lists are adding up and preventing you from spending time on what is important in your job, try delegating tasks for someone else to complete. This will help you free up time to work on the tasks that are most important in your role.

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10 Free Prioritization Templates to Organize Your Team’s Tasks

Praburam Srinivasan

Growth Marketing Manager

February 13, 2024

Picture a bustling intersection during rush hour. Hundreds of cars are lined up, and every driver is in a hurry to get to work on time. Without proper traffic regulations, complete chaos would ensue. 🚨

Prioritization tackles similar issues, but now we’re talking about looming deadlines and demanding clients instead of traffic jams. It provides guidelines for efficient organization when simultaneously juggling many tasks with different levels of urgency. 

To master your prioritization game, you must find a proper tool for the job. Check out the list of our favorite ClickUp priorities templates, and choose your sidekick! We’ll explore what each brings to the table and how you can use it to push productivity .

What is a Priorities Template?

What makes a good priorities template, 1. clickup project prioritization matrix template, 2. clickup simple priority matrix template, 3. clickup action priority matrix template, 4. clickup 2x2 priority matrix whiteboard template, 5. clickup prioritization whiteboard template, 6. clickup daily action plan template, 7. clickup smart action plan template, 8. clickup backlogs & sprints template, 9. clickup start-stop-continue template, 10. clickup simple to-dos template, top priorities templates: an overview.

Priorities templates are frameworks pre-built to help you organize assignments based on their urgency and business-impacting levels.

With all your tasks sorted, you and your team can work faster and with confidence. You know exactly what, when, and why needs to be done, so your efforts are purposeful and aligned with the company’s and team’s objectives. 

Using a project prioritization template has many benefits, including:

• Enhanced focus, efficiency, and productivity 
• Improved strategic decision making
• Effective resource allocation
• Reduced stress and better work-life balance
• Deadline adherence and timely goal achievement

Like prioritization techniques, priorities templates come in many forms with different criteria for evaluating tasks and can be used in various settings by people in specific organizational positions.

In general, an effective priorities template should check the following boxes:

• Visualization : Has a clear structure and an appealing design with attractive colors and elements to help bring your plans to life 
• Accessibility : Enables you to access and edit it from any device, allowing planning on the go
• Intuitiveness : Features a clean interface and is simple to use for all team members, new or experienced
• Flexibility : Accommodates different types of projects and teams and allows you to scale up as the business grows
• Integration : Supports integration with your favorite project management tools, allowing you to optimize the workflow
• Collaboration : Enables everyone from your team to participate in prioritization, offering advanced sharing, user control, and communication features

10 Project Prioritization Templates to Use in 2024

A sound task prioritization plan can set the stage for upcoming work so multiple projects run smoothly. By putting in the extra effort beforehand, you can save time during more demanding production stages—preventing miscommunication, delays, errors, and other mishaps that erode client trust. ⏰

Not sure how and where to start? Use one of these 10 ClickUp templates and optimize your work prioritization process in no time . They’re free to use, so you can explore different options with no strings attached!

The ClickUp Prioritization Matrix Template is an excellent way to visualize the complex hierarchy of upcoming tasks . 

It’s a 3×3 matrix with two axes representing Impact and Effort variables. A task can score low, medium, or high on these variables. The cells are strategically color-coded to help you assess the overall priority of assignments at a glance:

• Red : Do now 
• Orange : Do next
• Green : Do last

To add content to a ClickUp Whiteboard, create a new sticky note somewhere on the board and briefly describe the task. After assessing its impact and effort levels, drag and drop it to the cell that best represents its priority status. You can use the exact positioning of the note within the cell to introduce an additional ranking layer.

Being in Whiteboard view, the template allows freedom and creativity. You can add images and videos, link to tasks and docs, and doodle to express ideas to your team with a fun flare. 💥

If you prefer to stick with the tried-and-true Eisenhower priority matrix , the Simple Priority Matrix Template by ClickUp might be the tool to prioritize projects.

The template has a 2×2 structure and includes Urgency and Importance variables. Like the previous addition to our list, you can use the framework by adding sticky notes to its four cells. 

Depending on their positions, tasks can be classified as:

• High importance, high urgency: Do First
• Low importance, high urgency: Do Next
• High importance, low urgency: Do Later
• Low importance, low urgency: Do Last

Did you know you can use ClickUp to handle nearly all your behind-the-scenes operations, not only prioritization? 

Turn the sticky notes into tasks with one click and manage them all in List view. You can assess employee capacity, assign tasks, and track progress or add subtasks, checklists, attachments, and anything else aiding in task completion.

Another 2×2 matrix, the ClickUp Action Priority Matrix Template allows project managers to assess tasks based on their required effort and potential impact . 

Before deciding on their priority, list all tasks in the gray rectangle on the right side of the template using sticky notes. Feel free to change their colors to introduce additional information. For example, the color can indicate the assignee, department, or another evaluation factor. 

You can organize the tasks across four different cells, which have clever names:

• Big Projects : High impact + high effort
• Quick wins : High impact + low effort
• Fill-ins : Low impact + high effort
• Thankless tasks: Low impact + low effort

As with other ClickUp Whiteboard templates, you can customize almost every element to perfection. Feel free to go where your imagination takes you with images, videos, doodles, and diagrams. 🎨

As its name suggests, the ClickUp 2X2 Priority Matrix Whiteboard Template is a standard 2×2 matrix that you can drag and drop tasks in. 

Despite its familiar feel, the template has a unique approach compared to the previous recommendations on our list. Besides Importance, it includes Priority as one of the variables. This kind of structure is helpful when the task’s priority has been determined, but it doesn’t align with its overall significance.

While a task may be labeled as a high priority, that doesn’t mean it should be your primary concern. Use this template to figure out where you should allocate your time, considering the broader context.

The Prioritization Whiteboard Template by ClickUp provides a colorful and innovative way to manage tasks, ideas, and proposals within your organization. 🌈

Although similar to a matrix with its two axes, the template moves away from the strict categories and introduces a more flexible layer system. Depending on where you position them on the diagram, tasks can have the following statuses:

• Must Be Done/Feasible : Highly attainable and highly significant
• Great To Have/Needs Review : Highly attainable or highly significant
• Won’t Work/Impossible : Hardly significant or hardly attainable

Start by listing all your tasks as sticky notes in the Idea Pool section on the right. This prioritization template allows you to engage employees cross-functionally by assigning a note color to each department. After creating the list, assess the Significance and Attainability of tasks and move them to their respective positions.

Remember to update the legend on the left to help others get around the diagram more easily ! 🧭

We’re moving away from the simple project prioritization matrix template structure to a more comprehensive and interactive one. The ClickUp Daily Action Plan Template provides all the tools you need to boost productivity and efficiently reach your daily team goals . 📅

With this template, you can:

• Centralize all data to make it easily accessible to everyone
• Organize tasks and track their progress 
• Schedule and assign tasks and get a holistic view of the project timeline

The first thing you should do is define the high-end goal. You can do so in the small table at the top of the template’s List view. Next, define the tasks and subtasks for each department. Use the columns on the right side to set the deadline, indicate the task’s complexity, or introduce a custom field with other relevant information.

If you’re a fan of Kanban boards, you may find it more convenient to organize tasks in Board view. Whatever the layout, feel free to customize the grouping, filters, and other elements to tailor the template to your team’s needs. 

Thanks to the Timeline view, you can effectively manage time . By defining task dependencies, ensure tasks always follow the most logical and efficient order, even in the case of frequent rescheduling.

The SMART Action Plan Template by ClickUp gives you an opportunity to analyze your goals in-depth . You can specify the means of achieving them and measuring success. 📐

Since you’ve probably worked in Word, Google Docs, or a similar program, this template should ring a few bells! 🔔

Write down your thoughts in the designated text boxes, delete the banners, and you’re good to go.

Begin by defining your ultimate goal . In the following section, address the specific aspects of your action plan, following the SMART principle:

• Specific : What do you have to do to accomplish the goal? Who are the other individuals responsible, and what are their duties?
• Measurable : Which metrics will you use to track the progress and determine the success of your efforts? Are there any milestones that you must hit?
• Achievable : How do you plan on achieving the goal? What tools do you need? Which skills or knowledge do you have to brush up on?
• Relevant : Why does this goal matter? How does it fit in with the bigger picture and the company’s objectives?
• Time-bound : Are there any pre-set deadlines that you need to abide by? If not, how much time do you think you need to reach your target?

In the Follow-Through table at the end, report on your progress after pre-set time intervals. Outline key accomplishments, shortcomings, and areas that require additional support.

Software development team leads, listen up—this template is for you! The ClickUp Backlogs & Sprints Template is a comprehensive productivity tool based on Scrum and Agile methodologies . 

It occupies an entire ClickUp Space and offers various views and features, allowing you to manage the entire process from a single app. It might be challenging for beginners, but the included instructional Doc should provide enough guidance.

The template comes with several views, including:

• Epic List for a broad overview of all user stories
• Backlog Prioritization List to help you organize user stories and bugs 
• Sprint List and Daily Standup Board for long-term planning
• New Epic Request Form to collect user feedback
• Calendar, Timeline, and Workload views for scheduling 
• Retrospectives Docs to summarize and get input from teams

When it comes to priority management , the Backlog Prioritization List offers plenty of custom columns to help you evaluate tasks. You can assign Sprint Points, use the MoSCoW sorting method, appoint a priority tag, or introduce your own project criteria.

From time to time, you should re-evaluate your workflows to identify areas for improvement. After all, the task order doesn’t matter much if your processes are outdated and ineffective at their core. 

The ClickUp Start-Stop-Continue Template provides a framework for optimizing processes within your team or organization . It’s another Whiteboard template, so customization options are high.

First, establish the primary goal, i.e., what you wish to achieve. For example, that could be improving customer service or streamlining the onboarding process.

Create a list of tasks involved in the said process using sticky notes. Through discussion with your teammates or stakeholders, decide which category each task belongs to:

• Start : Tasks that you’re not doing but should be
• Stop : Ineffective tasks that take up a lot of time but bring little business value
• Continue : Tasks that have proven to be impactful and should remain in the process

Drag and drop the notes symbolizing tasks into their appropriate positions. You can turn the plans into action by creating tasks in List view, unlocking many other functionalities. Make sure to assess and upgrade the process again after a certain period to ensure its relevance.

In some cases, the simplest solution is the most effective one. The Simple To-Dos Template by ClickUp is a perfect example of that. It provides a straightforward way to manage tasks without unnecessary complexities or distractions .

Use the List view as your master task list. By default, the columns show the assignee, due date, priority tag, comments, and more. The status column features a dropdown menu with customizable categories. 

You can personalize the columns as well, choosing between 20 field options, including:

• Progress bar

Expand the information by adding subtasks, checklists, and attachments. Feel free to explore other views, such as Board and Gantt, and play around with the filters to find the optimal display variant. 

Not sure which template to try? Check out the following table for a summary of all options:

Supercharge Your Productivity With Priorities Templates

A solid prioritization system boosts your team’s performance, leading to faster and higher-quality output. It also lays the foundation for future work, allowing for efficient medium- and long-term planning. Task by task, project by project—your team can become one of the company’s top performers.

By using one of these awesome priorities templates, you can get a headstart and reach your productivity targets in no time! 🏁

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How to Ruthlessly Prioritize Tasks to Get More Done

Checking items off your to-do list is a beautiful thing—but it's also easier said than done. The best way I've found to make sure you complete your tasks is through ruthless prioritization. That means deciding not to do things you'd really like to do. It also means deciding what's the most important task even when everything on your list feels crucial.

But if you can prioritize until you have only one thing to focus on right now, you can't help but get to work. Use the tips and techniques below to help prioritize your tasks.

First, Consolidate All of Your Tasks Into a Single Source

Create trello cards from new labeled gmail emails.

Add new saved Slack messages to Todoist as tasks

Create Trello cards from new Office 365 emails

Once all of your tasks are on a single list, you can start analyzing and preparing to prioritize them.

Second, Analyze Your Task List

Do: complete the task now

Defer: complete it later

Delegate: assign it to someone else

Delete: remove it from your list

Go ahead and do any task that will take less than two minutes to complete. This is a quick way to narrow down your list to those harder-to-complete, higher-priority tasks.

Delegation is another great way to quickly cut down your task list. Assign delegatable tasks to others, and if there's anything you're waiting on others for, get it off your list. Create a calendar reminder to follow up on it later or add it to a shared project: Getting it off your task list will relieve the pressure of seeing it there all the time.

Next, delete anything that you really don't need to do. Things that are worth deleting include tasks that have been on your list forever that you never get around to or things that provide little value compared to the effort involved to complete them.

For example, say you have a task on your to-do list to go through your filing cabinet, scan all of your documents, and save them to the cloud. You've been looking at the task for years and have never made time for it. You probably never will. So instead of continuing to look at that task and feeling guilty for never getting to it, delete it.

If you don't want to forget a task you're deleting, move it to a separate "someday" list of things you want to do if you ever find yourself with time but nothing to fill it with.

Once you're finished doing, delegating, and deleting tasks, what you have left are your deferred tasks. These are all things you need to do that will take longer than two minutes to complete. With a master list of your deferred tasks in hand, you're ready to start prioritizing.

Six Methods for Prioritizing Your Tasks

1. use a priority matrix.

Anything that's due soon (or overdue) counts as urgent. As for what's truly important and what's more of a "nice to do" task—that's up to you to decide, but try to be as honest as you can.

This tool is particularly helpful for those times when you're drowning under a million things to do, as it helps you visualize what's really important and what can wait.

Once you've laid out your tasks, aim to get through the urgent and important tasks first so you're not butting up against deadlines. Then you can focus on the most productive quadrant: not urgent and important . These are the tasks that are easy to put off but provide lots of value when they do get done.

And whatever you do, avoid the busy work and time wasters that land in the not urgent and not important quadrant as much as you can.

2. Use relative prioritization

Categorizing your tasks using a priority matrix helps, but what if you still have dozens of tasks in your urgent and important queue? How do you know where to start?

A helpful next step is to assign each task a priority number. If you have ten tasks, each task gets a number 1-10. You can't have two number ones. The exercise here is to weigh each task against the others in order to determine where to start first. For example, say your list consists of three items:

File taxes (due today)

Complete an assignment (due tomorrow)

Put together a presentation (due next week)

By looking at all three tasks, it's obvious that filing your taxes needs to be your number-one priority: It's the most urgent and important task on your list.

For your other two tasks, it depends on context, and comparing your to-dos with each other often helps provide that context. Maybe the presentation isn't due until next week, but you need to get some information from your boss before you can start putting the presentation together. That might make it a higher priority than completing the assignment that's due tomorrow.

Complete an assignment

Put together a presentation

Request metrics for presentation

3. Make a prioritized task list for today

Assigning relative priorities works well if your task list is fairly static, but if you're adding several to-dos to your list every day, reprioritizing your list becomes another task in and of itself. If you have to manage a lot of incoming to-dos, it helps to make a prioritized task list each morning for the things you plan to do that day.

Look at your calendar and see how much time you think you'll have to devote to items on your task list today. Next, pick however many of the highest-priority tasks on your list that you think you can get through today. Ignore everything else you could be doing (until you're ready to plan tomorrow's list).

I like to include any calendar events on my "Today" list so I see an overview of my entire day and set my expectations accordingly. This also stops me from planning too many tasks on days I'm in meetings for hours.

A good rule of thumb when planning your day is to underestimate how much you can get done and overestimate how long each task will take. No doubt you've got plenty of things you can do if you happen to check everything off your list for today, which is a much better feeling that always moving unfinished tasks over to tomorrow.

4. Focus on your Most Important Tasks (MITs)

It's very simple: your MIT is the task you most want or need to get done today. – Leo Babauta, Zen Habits

When using MITs, your to-do list would have 1-3 of these, and anything else listed would become bonus, "nice to do if you have the time" tasks. You only work on bonus tasks if all your MITs are done—and if all you get through are your MITs, you've still had a successful day.

5. Pick a single thing to focus on

We're getting into ruthless territory now. When you're really struggling to get anything done, you should try this method, even if temporarily.

When you look at your task list, pick a single thing to focus on that day. It could be one big task you really want to get done, or it could be a theme that relates to several of your tasks, like "increase sales." Choosing a single task or idea to focus on can be a good way to remind yourself to stay on track whenever you find yourself getting distracted.

6. Find your 20% task

What's really tricky is working out what that 20% is that brings in the results. But once you do, you can apply the ultimate ruthless prioritization to your workday: Make that 20% work your priority—and your benchmark for a productive day.

The best way I've found to identify my 20% work is this simple exercise: First, ask yourself what you'd work on if you could only do three things today. Be ruthless; only pick three. Next, cut that down to two. And finally, just one. If you absolutely had to stop working after doing only one task, which would you do?

It's a really tough question to answer since we all have so many things to get through each day, but I've found it's a good way to realize which of your tasks provides the most value when it's finished.

For me, writing a new blog post would almost always be my 20% work since I get returns from writing in various ways—future SEO traffic, social shares, inbound traffic, more visibility for my personal brand and the site I'm writing for, and so on.

As you practice being ruthless with your to-dos, you'll find it gets easier, and you'll be able to pick the right method at the right time. And hopefully, you'll find that ruthlessly prioritizing tasks can actually be quite liberating.

Originally published in June 2017, this post has been updated by Jessica Greene with some additional prioritization techniques, plus a few new tools that help with the different techniques.

Related reading:

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Belle Cooper

Belle B. Cooper is the co-founder of Exist, a personal analytics platform to help you understand your life.

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Priority matrix: How to identify what matters and get more done

A priority matrix sorts tasks or projects by a defined set of variables, like urgency and effort. With this tool, team members can quickly determine what to tackle first. In this piece, we’ll discuss various types of priority matrices and explain how you can use them to accomplish more at work.

Project managers must have many skills to keep teams and projects on track. With so many moving parts, one of the hardest tasks is knowing what to tackle first. If a team member has two clients with high-priority projects, how can you help them prioritize and remain successful?

A priority matrix can help you sort your to-do list by things like urgency, importance, or impact. In this piece, we’ll discuss various types of priority matrices and explain how you can use them to accomplish more at work.

What is a priority matrix?

A priority matrix—also known as a prioritization matrix—sorts tasks or projects by a defined set of variables. Priority matrices can be simple or complex and may include anywhere from four quadrants to 20 rows or columns. 

In a four-quadrant priority matrix, your task may fall into four categories. For example, your quadrants may be:

High impact and high effort

High impact and low effort

Low impact and high effort

Low impact and low effort

By mapping your tasks along a priority matrix, you can determine how and when to tackle each to-do.

Priority matrix vs. Eisenhower matrix

Some people use these terms interchangeably, but a priority matrix is a broader framework that’s more versatile than the Eisenhower matrix model. The Eisenhower matrix is a simple priority matrix that has a time management focus. It maps tasks along a grid based on their urgency and importance.

When using the Eisenhower priority matrix, you’ll sort tasks by:

In an action-centered priority matrix, you’ll sort tasks by:

Investigate

When to use a priority matrix

Priority matrices are helpful when you need a quick solution to sort through and prioritize important initiatives. A priority matrix won’t help you solve complex calculations or actually make data-driven decisions , but it will help you create a map to get things done.

Bring out the priority matrix when you need to:

Prioritize tasks or projects 

Manage your time

Get your team on the same page

The priority matrix can be helpful when mapping out work schedules or workflows . It can also aid in conflict resolution , as it’s sometimes hard for teams to decide which projects or tasks to work on first.

How to use a priority matrix

The priority matrix is a versatile tool, and you can use it in various situations. Whether you’re sorting through your own tasks or managing team projects, the steps below will set you up for success.

1. Create a to-do list

The first thing you’ll need to do when using a priority matrix is make a list of things needing prioritization . This may seem like an obvious step, but many people don’t take the time to define their to-do list . By writing down the important tasks you have in front of you, you’ll have an easier time sorting through them and mapping them out. 

Your to-do list can include:

Team meetings

Client calls

Personal chores

You can create separate lists for internal and external work obligations (for example team-facing only and client-facing). You can also keep personal and professional items separate. However, it may be helpful to see how all your to-dos mesh together.

2. Identify your variables

Once you know the scope of your to-do list, determine the variables to measure your items by. To identify these variables, ask yourself: What qualities would a task need to be at the top of my to-do list?

Your answers may be:

It’s important

It has a lot of impact

It requires a lot of time

It requires a lot of effort

The deadline is approaching

Then, choose two of these qualities to measure your tasks. For example, you may decide that deadlines (in other words, urgency) and effort are the variables that apply to most of your projects.

3. Create your matrix

Before creating your priority matrix, decide whether you want it to be simple or complex. Both matrices will measure your tasks by the two variables you’ve chosen, but a complex matrix can help you get more precise about how urgent your tasks are and how much effort they take to complete.

If you choose a complex priority matrix, you may have five columns and five rows versus the standard one quadrant system of a simple matrix. Give your columns and rows labels so you know where to place your tasks according to their level. For example, you can assign levels of urgency and effort from high to low:

Required (5)

Significant (4)

Moderate (3)

Very High (5)

Very Low (1)

It’s also helpful to assign numerical values to each variable level. That way, you can multiply the corresponding numbers to find your task’s priority level in the grid. Once each of your tasks has a number, you can rank your tasks accordingly. For example, a task that is “required” urgency and “medium” effort would have a priority level of 15.

4. Place tasks in the matrix

Placing tasks in the priority matrix will involve some subjective decision making. Because this tool is a quick solution for getting things done , you’ll need to rely on experience and background knowledge as judgment. Place tasks in their appropriate order along the matrix according to the variables you have selected.

If you have two projects that seem tied in terms of urgency or high effort, dive deeper until you find a reason to prioritize one over the other. This is where other variables may come into play. For example, both tasks may be urgent, but one task may take priority over the other if it’s both urgent and more impactful than the other. 

5. Create an action plan

Once you’ve placed all of your tasks in your priority matrix, you should be able to visualize things more clearly. The matrix will show you what tasks to accomplish first and which tasks you have more time to complete. While this is a good starting point, the best way to expand on your priority matrix is to create an action plan . 

An action plan does more than show you which tasks to complete first—it helps you outline exactly how you’ll accomplish your goals. To create an action plan using the tasks from your priority matrix, you’ll:

Set SMART goals

Allocate resources

Create deadlines and milestones

Monitor and revise your plan as needed

Use task management software to streamline your action plan in a central source of truth. That way, you can communicate and track items with your team.

Priority matrix example

We showed a comparison above between a simple and complex priority matrix. Here’s an example of a complex priority matrix using urgency and effort as two variables of measurement. Numerical values and colors are included to make the tasks easy to sort through.

The original to-do list for this matrix may have looked like this:

Plan team workshop

Finish budget proposal for Client A

Onboard new hire

Send performance reviews to the department head

Write an ebook for company website

Edit whitepaper for Client B

Sign new hire documents

A prioritized version of the to-do list would look like this:

Finish budget proposal for Client A (20)

Onboard new hire (15)

Write ebook for company website (15)

Edit whitepaper for Client B (12)

Send performance reviews to the department head (10)

Sign new hire documents (8)

Plan team workshop (6)

Onboarding a new hire and writing an ebook for the company website both have a priority level of 15. Onboarding a new hire would ultimately come first in the to-do list because it’s more urgent than writing the ebook. Urgency is often the most important variable in the priority matrix.

How Asana uses work management for project intake

Learn how Asana's PMO leaders streamline intake and prioritize the right work for the business.

Pair your priority matrix with a task management tool

Using the priority matrix to sort through your tasks is an important step, but only the first one. Now that you know what to do first, it’s time to get to work. When you pair your priority matrix with a task management tool , you’ll feel supported through your workflow from start to finish. Aside from mastering project prioritization, Asana lets you track tasks, delegate subtasks, and set deadlines to make sure projects get done on time.

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How to prioritize tasks when everything’s important

“busyness” doesn’t always equate with progress—learning how to prioritize tasks will help you make the most of your workday.

During the workday, tasks are often prioritized (or not) according to the needs of others or the immediacy of deadlines. This can happen in our personal lives, too, with limited time spent on activities that are actually important , and more energy spent being “busy.” Prioritizing tasks effectively—with intention and according to future goals —can change this, ensuring that every task you tackle drives value and keeping unimportant tasks from cluttering your to-do list. 

By implementing prioritization strategies, you can drastically change the arc of your workday to really make the most of your time in the office —and at home. Whether you’re a sole proprietor or an executive at a Fortune 500 company, these strategies will help you evaluate and set your top priorities. 

Seven strategies for prioritizing tasks at work   

Thoughtful prioritization typically involves creating an agenda, evaluating tasks, and allocating time and work to bring the most value in a short amount of time. Prioritization should be flexible, as you may need to interrupt low-priority tasks for urgent must-dos. 

1. Have a list that contains all tasks in one 

Effective prioritization comes from understanding the full scope of what you need to get done—even the most mundane tasks should be written down and considered. To give yourself a complete picture, it’s a good idea to include both personal and workday tasks in a single task list. 

Everything from picking up your dry cleaning to scheduling a one-on-one meeting with your boss should be captured in the same place. Once everything is written down, prioritization typically happens according to the importance, urgency, length, and reward of each task.

2. Identify what’s important: Understanding your true goals 

While it might seem like an immediate time management strategy , prioritization is key in achieving long-term goals. Understanding what you’re really working toward—be it a promotion, a finished project, or a career change—helps you identify the tasks most pertinent to those future outcomes. It can be a good idea to break these larger goals into smaller, time-related goals. For example, a yearly goal can be deconstructed into monthly to-do lists, which then lead to weekly tasks, daily priorities, etc. 

For Alejandro Cerecedo, a senior fashion account executive at PR firm  Another Company  and a member at  WeWork Reforma 26  in Mexico City, setting long-term goals is how he aligns and motivates his team at the beginning of each year. “We talk about their personal and professional goals, and we set a timeline for how we’re going to achieve them,” Cerecedo says.

This big-picture thinking is vital in prioritizing effectively: It’s a common misconception that being busy equates with progress. However, filling your day with tasks that have no impact on an ultimate goal is time wasted . Be honest with yourself about the lasting value of each task, and always have the end-goal in mind.  

3. Highlight what’s urgent

Your to-do list should provide full visibility of deadlines, helping you to identify which tasks must be completed promptly and to plan ahead according to future deadlines. 

Creating deadlines even when they’re not formally required is also important; otherwise, you will continue pushing back important tasks simply because they aren’t time-sensitive. (This strategy can also be helpful in increasing productivity and reducing procrastination.)

4. Prioritize tasks based on importance and urgency 

In his 1989 book The 7 Habits of Highly Effective People , businessman and keynote speaker Stephen Covey suggests tasks should be categorized (and then prioritized) according to importance and urgency.   

• Urgent and important: These tasks should be done first
• Important but not urgent: Block off time on your calendar to get this done, without interruption
• Urgent but unimportant: Delegate. Delegate. Delegate. 
• Neither urgent or important: Remove from your to-do list

Another strategy for ensuring important tasks are prioritized —even above asks from pushy stakeholders or “urgent” ad-hoc requests—is the Most Important Tasks (MIT) methodology . This strategy involves creating a separate list of just three tasks that must be done that day. These tasks should be chosen more for their importance than their urgency. To decide, ask yourself goal-oriented questions: What tasks will have the biggest impact on the end result? What can I get done today to further my progress toward that goal? 

5. Avoid competing priorities 

When the tasks you’re working on aren’t particularly difficult, it’s relatively easy to manage them in tandem. However, as difficulty increases, research shows people who are in positions of power are more likely to prioritize a single goal, whereas people in low-powered positions will continue to try and manage multiple priorities. This dual-task strategy has been linked to a decline in performance, which means the most important tasks aren’t fulfilled to the highest standard. 

A tactic for staying focused on one important task at a time is identifying likely distractions—concurrent tasks or ad-hoc requests—and actively avoiding them throughout the day. This means if you’re tasked with pulling data for a project at the same time you’re creating slides for a presentation, you should prioritize one task and avoid any work, emails, messages, or preparation related to the other. 

6. Consider effort

When staring at a long to-do list, it’s easy to become overwhelmed by the work that needs doing—a feeling that reduces productivity and leads to procrastination. A strategy to overcome this involves evaluating tasks according to the effort required to complete them. 

If your to-do list is becoming too burdensome, prioritize those tasks that require minimal time and effort and move through them quickly. This clearing of tasks will give you some breathing space and generate a sense of accomplishment to propel you throughout the day. 

7. Review constantly and be realistic

One of the steps in the five-step “Get Things Done” (GTD) methodology from productivity consultant David Allen involves critical reflection. Frequently reviewing your task list and priorities is key in “regaining control and focus”, Allen argues .

Quick tips for effective task prioritization

As you realize the necessity of proper prioritization, it can suddenly feel more complicated—and more stress-inducing—than creating a simple task list. The key strategies mentioned above are summarized below, to help you set your priorities with intention. 

• Write everything down: Personal and work tasks should be captured in one place.
• Evaluate long-term goals: Consider your larger long-term goals, and the work you need to do to reach them. 
• Break down larger goals: To understand how to achieve your long-term goals, break them down into yearly, monthly, and weekly achievements. 
• Create clear deadlines: Give yourself full visibility of deadlines, and create deadlines for yourself when none are formally required. 
• Employ the urgent-versus-important method: Prioritize urgent and important tasks; set a specific time to work on important nonurgent tasks; and delegate or remove all other tasks. 
• Create a daily MIT list: Write down three important tasks that should be done that day. These tasks should always relate to your larger, future goals. TEST
• Avoid distractions: Intentionally steer clear of competing tasks, especially as task difficulty increases. 
• Consider effort: When your task list is becoming too much, prioritize according to effort and breeze through those easier tasks more quickly.

Prioritize your time and be realistic 

No matter how well you prioritize, there is only so much you can achieve in one day, and certain distractions are impossible to avoid. It’s important to be realistic in setting goals and prioritizing tasks. Otherwise, you’ll create false expectations of those around you, and you’ll constantly feel as if you’re falling behind.

Remember, the purpose of prioritization is to spend time working on the important tasks, those things that will make a difference in the long run and move you in the right direction. When prioritization is handled well, you’ll feel less reactive and more focused and intentional. The aim is to complete work that signifies true progress, and let all the rest—all the “busyness”—fall to the wayside. 

This article was originally published on February 6, 2020, and has been updated throughout by the editors.

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Caitlin Bishop was a writer for WeWork’s  Ideas by We , based in New York City. Previously, she was a journalist and editor at  Mamamia  in Sydney, Australia, and a contributing reporter at  Gotham Gazette .

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The Ultimate Guide for How to Prioritize (When Everything's a Priority)

How to prioritize your tasks

How to prioritize tasks with a framework, how to prioritize projects with airfocus, how to prioritize your time, ready to take the stress out of prioritization.

Do you ever feel like you’re spinning plates?

We’re not speaking in literal terms, of course — unless you happen to be a circus performer. 

Instead, we’re referring to those hair-pulling moments in life, and especially at work, when it feels like you’ve got a million things to do at once and you need to keep them all in balance.

But you can’t just let a single plate drop because that could lead to a catastrophic domino effect resulting in, well, a lot of smashed plates. No, to keep every single one of those plates spinning, you need to know where to direct your attention — and when.

So, let’s set down those plates just for a few minutes, take a deep breath, and discover exactly how to prioritize — even when everything feels like a priority.

Get started with prioritization templates

.css-uphcpb{position:absolute;left:0;top:-87px;} The science behind prioritization

Some people are blessed with a natural talent for organization and management.

Some people are not.

If you fall into the latter category, the skill of task and project prioritization may seem like nothing short of an art form. 

But in truth, it’s more of a science.

Sure, when we witness a master of prioritization at work — effortlessly gliding from task to task, without breaking a sweat — it can seem like magic. But the process actually can be broken down in a logical, straightforward way. 

And effective prioritization is a reward in itself. Not only will you feel really on top of what you have to do, when, and how , but you’ll find you have much more breathing space throughout the day. 

In fact, becoming a master prioritizer can help you:

1.   Maintain the focus on long-term goals. To-do lists are great but not as great as a prioritized list of tasks. After all, it’s too easy to jump from task to task, at random, when your to-do list has no structure. With your priorities clearly in place, you know you’re doing the right things at the right time to work toward your long-term goals.

2.   Prevent burnout and reduce stress. Did you know that stress is responsible for a loss of $300 billion every year in the USA ? With that sort of money on the line, it’s surprising that more employers aren’t focused on training their staff in effective prioritization strategies. Even the simplest of prioritization frameworks can reduce workplace stress and prevent staff from burning out.

3.   Improve time management and minimize wasted time. Poor time management can also be damaging to an organization. Just imagine how many minutes are lost when employees aren’t sure what to do next and why. Again, prioritized task lists solve this elegantly and bring much-needed order and structure to the working day.

4.   Effectively avoid that “firefighter” feeling. Have you ever had one of those days where you feel like you’re running from job to job, solving issue after issue? If so, then you already know that firefighter feeling. The problem is, tackling critical tasks in this last-minute way is not conducive to good business, and it can lead to even more stress. With a good prioritization system in place, you can leave the firefighting to the professionals.

Now that we’re all a bit more familiar with the why of prioritization , let’s take some time to look at the how . We’ll focus on professional settings — and specific roles, such as project managers — a little later, but for now, let’s consider the basics.

How do you prioritize your everyday tasks?

Whether it’s walking the dog, washing the car, posting a letter, or clearing your inbox, we all have a seemingly never-ending list of tasks to take care of. 

But here’s the question: how do you go about tackling them?

If you’re familiar with the adage about eating an elephant, then you’ll know how it’s done: one bite at a time. So let’s summarize a solid process for prioritizing your day-to-day tasks — at work or home:

1.   Make a list of all of your tasks. Sometimes getting each item down on paper can be a big relief — and it’ll help ensure you don’t miss anything crucial.

2.   Decide which of your tasks are urgent. As you start to get your to-do list down on paper, you’ll naturally have a sense of which of them need taking care of first. If there are any tasks on your list that either have critical dependencies or simply need to be completed as soon as possible, these should be noted.

3.   Assign a value to each task . Outside of those critical and time-sensitive tasks, and efficient way to prioritize other items is by how valuable they are to you personally or your organization. Understanding which tasks are better aligned with the bigger picture — specifically within a business — is a valuable skill.

4.   Organize remaining tasks by effort and time . At this point, you should have a fairly well-ordered list based on urgency and value — but how do you sort the remaining items? One solid option is to decide how much time and effort it will take to complete them. Because these will usually be lower-priority items, you’ll have the luxury of deciding whether you want to tackle high or low-effort tasks first.

With this fundamental system of prioritization, you stand a solid chance of reducing stress and — importantly — getting stuff done .

If it still seems a bit daunting, here's a bonus tip for you: don’t be afraid to delegate or eliminate tasks . For example, if a low-priority item has been hanging around on your list for longer than you can remember, chances are you’ll probably never get around to it. So, you can either be brave and remove it from your list entirely — or simply ask someone else to do it. 

Sometimes the simple solutions really are the best.

If, like us, you get a kick from structuring your workflow, then you’ll love these prioritization frameworks . 

When you want to double-check the validity of your self-set priorities, you can use any of these three frameworks to view your must-haves versus the nice-to-haves in a new light.

The Kano Model

Originally developed by Dr. Noriaki Kano at Tokyo University of Science back in 1984, the Kano Model is a prioritization framework that focuses on the satisfaction — and delight — of the customer.

Download Now: Get our 5-minute guide on How to use the Kano Model

Through a product management lens, the Kano model allows product managers and owners to prioritize specific product features based on how customers will react to them. The core metric for the Kano model is customer delight, and the question you should ask of any feature is, “How much will a customer care about this feature?”

It offers several categories , into which you can assign your specific tasks:

“Must-be” : the absolute basic features a product must have in order to hit baseline. If a product doesn’t have these, customers will notice, and delight will be negatively impacted — but they may otherwise go unnoticed.

"One-dimensional” : features that deliver proportionate increases in customer delight in a linear fashion. For example, granting a user additional storage space in a cloud storage app.

“Attractive” : essentially nice-to-have features guaranteed to increase customer delight — but that won’t necessarily be missed if they aren’t included. 

The RICE Framework

Even when you have every single outstanding task listed clearly right in front of you, you can still experience that pang of panic when you ask: where the heck do I begin?

The RICE framework aims to alleviate this stress by giving you a framework of metrics by which you can categorize — and prioritize — your task list. As you might imagine, the name “RICE” is an initialism representing the four categorization metrics you can use to do this:

Reach . This criterion asks a simple question: how many customers will be positively impacted by this task? It may seem like common sense, but the more users a specific feature will benefit, the higher its priority should be.

Impact . Even if your feature has a large reach, it might not necessarily be too impactful, which is where this metric comes in handy. Impact generally refers to how much of a net gain a feature will result in, be that increased sales, increased daily users, and so on.  

Confidence . You’ve taken the time to calculate the Reach and Impact of a specific task or feature — but how confident are you in these decisions? The Confidence metric allows you to account for varying evidence levels for both previous metrics.

Effort . In a nutshell, the Effort metric asks you to consider the resources that will be required to tackle the tasks you prioritized with the previous three metrics. No matter how strong the case is for Reach, Impact, and Confidence — if the Effort is too high, it still might not be worth prioritizing. 

If you’ve been struggling to find a reliable method to slice and dice your task list, the RICE model might be something of a lifesaver. 

The Weighted Scoring system is something we hold close to our hearts here at airfocus because it’s built into the very core of our platform. 

How does it work? Well, it’s particularly useful in the context of product management because it allows you to assign an importance value — or weight — to the specific categories of tasks you have outstanding. These categories are known as criteria, and you’ll assign a certain percentage to each one based on how important it is to your overall project. 

For example, if a certain feature is essential to your user experience, tasks related to it will be given more weighting. 

Once you’ve agreed upon your set of criteria, you’ll need to score each of your outstanding tasks using that criterion. Take each of your tasks and assign a numeric value to it, to represent its weighted percentage value. 

When all of your to-be-prioritized tasks have been weighted , you’ll be able to sort your list of tasks by the weighting and see exactly where your team should focus their efforts first. 

And one of the fastest ways to do this is by using airfocus...

If you’re new to airfocus , you may not be familiar with just how easy it is to prioritize an overall project (and all of the smaller tasks it comprises). So, let’s do something about that.

Below is a step-by-step guide showcasing how quick and simple it is to use airfocus to prioritize your projects at work:

First, log into airfocus as normal.

Once you’re looking at your workspace, click the Add Item button to create a new task to be prioritized.

Once you’ve added your new item, you’ll be able to rate the task by each criterion (or column) in the row. By default, airfocus prioritizes tasks using a combination of strategy criteria (for example, Revenue Increase) and factors (such as Value and Costs).

All of the factors and criteria in airfocus are automatically weighted equally, but you can click the Prioritization settings button at the top of the screen anytime to enable custom weighting.

The main factors in airfocus (Value, Costs, and Must) have scoring direction presets to help immediately prioritize your tasks, but you can also make more granular prioritization changes by manually sorting by criteria. To do that, simply click the name of the criteria at the top of a column ( Revenue Increase , for example), then choose to Sort ascending or Sort descending . This level of fine-grain control makes multi-layered prioritization that much easier with airfocus.

Of course, you’re free to rename factors and criteria any way that you like — and define the weighting of each one, too. We designed airfocus to be completely customizable, so no matter how you need to slice your task list, you’ll be able to prioritize like a pro.  

Visualizing your project (the easy way)

Once you’ve added all of your items to airfocus and seen first-hand just how easy it is to prioritize them, it’s a good idea to explore your visualization options. After all, we interpret information in different ways — and the airfocus view options menu is a great tool to account for this.

Here’s a quick summary of each of the views you can switch to at the click of a button:

 Item View is the best way to take a task-based approach to your project. If your focus is on the individual steps needed to complete the project, Item View displays outstanding items as a prioritized list. Even better, you can use filters and sorting options to reorganize your data by Risk, Value, and Effort — making it easier to focus on what matters most to you.

Chart View is a great option for prioritization purists. If you’re looking for a way to visualize your entire project based on the importance of each of the tasks, the Chart View in airfocus allows you to do exactly that.

Board View is the best option if you’re looking to zoom out and see the long-term outlook for your project. It’ll show you outstanding tasks, which ones are currently in progress, tasks which are due to land, and those which you might consider cutting entirely. 

Timeline View is perfect if you need to visualize exactly how each of the tasks in your project interacts and overlaps with one another. You can structure your view by the buckets which are the highest priority to you, then see exactly where each assigned task falls on the timeline. Need to shuffle tasks around? No problem. You can do it in just a few clicks. 

Need to prioritize in a hurry? Try a template

As you’ve seen from the step-by-step process above, airfocus offers powerful prioritization features to help you turn you into the project management maestro you were destined to be. But what happens if you’re in a rush and you just need to prioritize your tasks, assign them, and get going? Don’t worry — we’ve got you covered with our range of handy templates. 

Here are some of our favorite templates:

The Product Roadmap and Prioritization Template is an excellent choice if you’re managing a long-term product roadmap. Serving as the single source of truth for the whole project, this template is ideal for product managers and product owners and gives you advanced options like the ability to sort by Value, Cost, or Bubble (must-have). If you need to take a long view of your product roadmap, this free template is essential.

If you’re just starting out with your business, the Startup Roadmap and Prioritization Template can be an invaluable tool. As you’ll almost certainly already know, startups have a lot of moving parts — and keeping everything in balance can be a tall order. If you’re feeling the pressure, we’ve got you covered with this template. Ideal for founders and their startup teams, the template will help you prioritize your tasks and focus on customer growth and customer value. 

Okay, we’ve gone through a lot so far — are you still with us? We hope so.

The benefits of prioritization should be plain to see at this point, along with the many strategies and tactics you can deploy to do it right. That said, there may be a certain lingering question in your mind: how do I prioritize my time?

Naturally, time is our most precious resource, and all the prioritization frameworks in the world will be for nothing if you don’t have time to complete your tasks. And, unless you have awesome supernatural powers (or a DeLorean), there’s no way to simply create more time, so that just leaves — you guessed it — prioritization !

Here are our top 3 suggestions for making the most of every minute and ensuring you’re a productivity machine:

Use a timer to make every minute count. By tracking how long your tasks take, you’ll be better placed to prioritize them in the future. This can also be a great way to become super-productive — it’s the entire basis of the “Pomodoro method.”

Become ruthless about delegation. We already touched on the power of either removing tasks or delegating them, but it bears repeating. There is simply no better way to save time than eliminating a task altogether — but you have to be 100% you’re doing it for the right reasons. Otherwise, it’s just sweeping it under the rug.

Block out your calendar ahead of time. Having all of your tasks listed out is one thing, but actually getting them done in the time you have available? That’s something else. This “time trap” can actually lead to a great deal of stress, so one tip to avoid it is to assign a time estimate to each task, then plan your day accordingly by slotting tasks into each hour.

You should now be a fully-fledged master of prioritization, and while we can’t guarantee that you’ll be flitting from task to task with effortless grace right away, we certainly hope you’ve learned how to manage your projects a little better.

Don’t forget: if you’re sick of keeping those plates spinning, there’s never been a better time to give airfocus a try — and discover the easy way to prioritize your life.

Andrei Tiburca

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How to Prioritize Work and Meet Deadlines When Everything Is #1

Knowing how to prioritize work affects the success of your project, the engagement of your team, and your role as a leader. All projects—especially large, complex projects—need clear priorities. Easier said than done. Especially when every task appears to be priority number #1 and screaming for your attention. You can count on technical projects, no matter how well-planned, to involve change orders, re-prioritization and the regular appearance of surprises. It’s just the natural order of things.

Prioritization is the process of determining the level of importance and urgency of a task, thing or event. It’s a key skill for any working professional and is absolutely essential for project managers to master. Smart prioritization is a vital part of LiquidPlanner’s Planning Intelligence philosophy to align people, priorities and projects.

One of the biggest challenges for project managers and team leaders is accurately prioritizing the work that matters on a daily basis. Even if you have the best project management software , you’re the one who enters information into the tool. And, you don’t want to fall into the role of crying “top priority” for every other project that comes down the pike.

Just as you have to be diligent and have the right kind of project insight to ensure that nobody’s working on yesterday’s priorities. It takes a lot of practice and time management to get this right.

To help you manage your team’s workload and hit deadlines on time, here are 6 steps to prioritizing projects that have a lot of moving parts.

1. Collect a list of all your tasks. 

Pull together everything you could possibly consider getting done in a day. Don’t worry about the order or the number of items upfront. This will help you frame up how and when to allocate your time wisely.

Having trouble organizing your tasks in one central location? Check out LiquidPlanner’s  project management software  which uses smart prioritization as one of it’s 6 Planning Intelligence solutions. It helps teams come up with more realistic estimates for your tasks while answering the question, when?

2. Identify urgent vs. important.

The next step is to see if you have any tasks that need immediate attention. We’re talking about work that, if not completed by the end of the day or in the next several hours, will have serious negative consequences (missed client deadline; missed publication or release deadlines, etc.).

Allocate time to prioritizing your most urgent tasks earlier in the day. If you push these to a later period, you’re at risk of being too busy as the day runs on. Prioritizing based on urgency also alleviates some of the stress when approaching a tight deadline or high pressure workload demands. 

Check to see if there are any high-priority dependencies that rely on you finishing up a piece of work now. Be sure to contact any member of your team that can help finish any dependencies earlier in the day.

3. Assess the value of your tasks.

Take a look at your important work and identify what carries the highest value to your business and organization. As a general practice, you want to recognize exactly which types of tasks are critical and have top priority over the others.

For example, focus on client projects before internal work; setting up the new CEO’s computer before re-configuring the database; answering support tickets before writing training materials, and so on. Another way to assess value is to look at how many people are impacted by your work. In general, the more people involved or impacted, the higher the stakes.

Below are some helpful references to assess the value and importance of your tasks. 

• Critical priorities are time sensitive and high value. These include tasks dealing with crises or strict client deadlines.  
• High value tasks that are not time sensitive should be considered high priorities . These are tasks that involve thinking, planning and collaboration.  
• Medium priorities can be time sensitive but not high in value. Meetings, email communications, and project organizing can fall into this category. 
• Low priority projects and tasks are ones that are not time sensitive and do not have high value. You can push these priorities later in the week or drop them entirely.

4. Order tasks by estimated effort.

If you have tasks that seem to tie for priority standing, check their estimates , and start on whichever one you think will take the most effort to complete. Productivity experts suggest the tactic of starting the lengthier task first. But, if you feel like you can’t focus on your meatier projects before you finish up the shorter task, then go with your gut and do that. It can be motivating to check a small task off the list before diving into deeper waters.

Managing uncertainty is hard work. If you’re looking for more information on creating accurate project estimates, check out how LiquidPlanner utilizes Smart Estimation through our project management solution, Planning Intelligence. 

5. Be flexible and adaptable.

Uncertainty and change are given. Know that your priorities will change, and often when you least expect them to. So plan for the unexpected. But—and here’s the trick—you also want to stay focused on the tasks you’re committed to completing. While working on such tasks, try to forecast other project requirements that will follow your priorities so you can better prepare for what lies ahead. 

6. Know when to cut.

Be realistic. You probably can’t get to everything on your list. After you prioritize your tasks and look at your estimates, cut the remaining tasks from your list, and focus on the priorities that you know you must and can complete for the day. While cutting your prioritization list down, focus on the main things that will bring you feelings of accomplishment for the day. Then take a deep breath, dive in, and be ready for anything.

Get Smart Prioritization With LiquidPlanner

When everything feels like number one on your to-do list, it can be overwhelming. But remember, not everything on your list is, or should be, a priority. This is why it’s important to know how to identify your priorities and to differentiate between critical and low priorities. 

With LiquidPlanner’s Planning Intelligence software, your organization’s project management priorities are factored into the schedule from the very beginning. Learn about our impact on your project portfolio when priorities shift by starting a free trial today.

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When you worked on multiple projects, how did you prioritize? Interview questions answered

Corporate managers love to bury their employees under a heavy workload . They assign you yet another client , or one more project. You are working overtime already–they know. But why wouldn’t you work from home, or manage your time in work better ? You are young, and you can surely handle a heavy workload. And if you cannot, they will simply chew you and squeeze you to the maximum, until you cannot stand it any longer and leave the place, or experience a complete burnout…

This isn’t the most encouraging opening to an article about an interview question–I know. But mark my words: if they ask you in an interview about managing multiple projects or working on several project simultaneously, it’s not necessarily a good sign , and you should consider twice whether to accept their offer or not. They may also structure the question differently, for example “ Tell me about a time when you had too many things to do and you had to prioritize your tasks “, or “ How do you handle multiple tasks at once? ” Regardless of the wording though, they are always looking for the same thing.

Let’s have a look at 7 sample answers to this tricky interview question. My list includes some popular choices, options for people who lack any previous working experience , and also some rather unconventional answers that may leave your interviewers puzzled –something you may want to achieve in certain circumstances. Let’s have a look at the answers.

7 sample answers to “When you worked on multiple projects, how did you prioritize?”

• I always try to have a to do list in work . I assign low, mid, or high priority to each task on the list–not to the entire project. And then I work accordingly–taking care of the tasks with highest priority first, regardless of the project they belong to. Of course if I got a call from a manager or a specific deadline was set for me to deliver some report or analysis, I prioritized it to other tasks to ensure I’d meet the deadline. In my opinion, the most important thing is to have a meaningful system in your work , something you can rely on when you aren’t sure what to do next. That’s what I always try to do.
• This is my first job application , so I have not yet had to handle multiple tasks at once. But I recall my school times, when we had to prepare for different exams , plus of course I had my duties outside of school. I think it is important to set your priorities clear –for me school was my first priority, and hence I prioritized my student duties to everything else . In the workplace probably my manager will set the priorities, or I will decide about them based on certain criteria. Once it is clear what has a priority, it’s easy to decide on which project I should work each day in the job.
• To be honest I actually struggled to prioritize , and that’s one of the reasons why I am here today. They assigned me to too many projects in my last job. I was getting 100+ emails daily , from different people involved in different projects. I also had to participate on several short meetings, almost daily. And to tell the truth most of them were pointless . When you sum everything up, I actually didn’t have time to do the real work –I was just attending meetings or answering emails. Prioritization was out of question . I tried to explain this to my manager but they did not get it. Hence I left them, and I am looking for work in some place with a better management.
• I divided my day in work to three parts. Early morning was the most productive one. I arrived before anyone else, nobody bothered me with anything, and I could work on some tasks that demanded creativeness or a lot of thinking, or a quiet office . That’s when I worked on the most important tasks in all my projects. Later during the day when the office was buzzing with people and everyone wanted something from me , I spent time responding to emails and internal communication, and taking care of easier administrative work. Then later in the afternoon when the atmosphere calmed down again, I focused on more creative tasks again, working always on the one with the closest deadline .
• I’ve never worked anywhere, but I guess the entire life is about prioritization . We try to juggle our roles in life –a son, a father, a colleague, a friend, a husband and perhaps even a lover… depends on how many of them you have. The more balls you have in the air, the more difficult it gets. My personal philosophy is to try to find balance in life . I mean, you should not give all your time to work, or to your wife, totally neglecting all other bonds and relationships. And you should always try to find some time also for your own hobbies , when you do something you love. In my opinion, a similar attitude may work well in the job. Instead of prioritizing one project to another (which will certainly result in a neglect of some duties in the later ), I’d prefer dividing my time in work, and give some time to each project and each manager–so we progress on all fronts… But as I said, I am new to the workforce. I’d gladly learn from more experienced managers how to prioritize my work in a most effective way , while working on multiple projects.
• To be honest, it depended on the client, or my manager–who was more demanding , or threatened to fire me, or similar stuff. I know this isn’t the right way to prioritize in work once you have too many things to do . Yet in worked that way in my last job, and I am pretty sure it works that way in many other companies. It is not easy to get over our ego , and my former managers had big egos. And since I knew it was wrong, and could not stand this model any longer, I decided to quit the company. I hope that in your corporation you have a better system in place , or a better management, and I will be able to prioritize work according to milestones and deadlines and urgency, and not the wishes and threats of the managers.
• I worked under an autocratic leader in my last job. It had certain disadvantages, but at the same time it was easier to prioritize, because they always told me exactly what I should do . They loved to have everything under control, completely. Hence I simply worked on the task they assigned me to at each moment, and when I was done with it, I asked what I should do next. It was as simple as that. Having said that, I feel ready to decide on my own , and if I should prioritize the work I will decide according to deadlines and importance of each task on my list.

It is hard to work effectively without having a good system in work

You can prioritize your work according to deadlines, importance of each task, or even according to how you feel, and what task you can realistically do at a certain time in work.

Each of that is fine, as long as you have a system , some criteria, and can decide on your own. Ensure the hiring managers that you do not rely on luck, coincidence, or a flip of a coin. You have your way of prioritizing work, and just as you followed it in your last job, you can do so in their place.

No experience? Refer to school or duties of everyday life

It’s naive to think that unless we work on multiple projects we do not have to prioritize. Even if you work on a single project, you have to make choices : whether to answer emails firstly, or go to a meeting, or work on that report you hate doing, or forget everything and have a coffee and a cigarette .

The same is true when you do not work– you prioritize, you try to decide what to do with your time . Whether to use it effectively, and with whom, or kill it sleeping in bed or watching some Netflix series that makes people more sheepish than they already are…

You can refer to these things when you lack working experience. You can either talk about prioritizing school to the rest of things (sample answer no. 2), or about balancing the roles and duties you have in life (sample answer no. 5).

* Special Tip: This isn’t the only difficult question you will face while interviewing for any decent job. You will face questions about prioritization, dealing with pressure, dealing with ambiguity , and other tricky scenarios that happen in the workplace. If you want to make sure that you stand out with your answers and outclass your competitors, have a look at our Interview Success Package . Up to 10 premium answers to 31 tricky scenario based questions (+ more) will make your life much easier in the interviews. Thank you for checking it out!

Failing to prioritize (not necessarily a result of your incompetence) can be the reason why you left your job

As I wrote at the beginning of this post, many managers expect too much from their employees. They will assign you more work all the time, watching you struggle or even suffer, until you break. And then they simply send you packing and employ another naive graduate . Hard to read such words? Well, welcome to the corporate world!

But it’s not like that in every company . And it can definitely be a reason why you failed to prioritize your work and reach your goals, as well as the last nail to the coffin, one after which you realized enough is enough, and left the place.

It’s completely fine talking about such things in an interview. Hiring managers like honest job applicants , and more importantly–you clearly explain what you do not want to experience anymore . So if that’s what they expected from you–to work like a horse and stay overtime each day, they won’t hire you–which is good for you, because you do not want to experience such kind of pressure again. Check sample answers no. 3 and no. 6 for some inspiration.

Ready to answer this one? I hope so! Check also 7 sample answers to other tricky interview questions :

• Tell us about a time when you struggled to meet a tight deadline in work.
• How do you handle multiple deadline?
• Time management interview questions .
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How Do You Prioritize Your Work? (Interview Question)

By Biron Clark

Published: November 15, 2023

Most jobs require you to manage multiple tasks, and you’re going to face competing priorities. So employers ask interview questions like “How do you prioritize your work?” or “Tell me about a time when you had conflicting priorities at work.”

And if you can’t show the employer that you’ve got a proven system for time management and task management, they’re going to be worried.

(Which could cost you the job offer.)

Coming up, I’ll show you how to answer interview questions about conflicting priorities so you can impress the interviewer and land the job.

What Does “Conflicting Priorities” Mean?

Conflicting priorities are objectives competing for your time and attention that cannot all be done at once. When faced with conflicting priorities, you’re forced to manage your time and rank those tasks in order of importance, addressing some before others.

Employers will often ask an interview question about competing priorities to make sure you’ll be able to handle the most important tasks they assign you and complete projects within their deadlines. To properly handle conflicting priorities, you’ll need time management skills, communication skills (if working as part of a team), the ability to stay calm while working under stress , and problem solving ability.

Examples of Conflicting Priorities at Work

• Your direct manager told you that Task A needs to be done immediately, but a Department Head, who works above your boss, pulled you aside and asked if you could help them with Task B.
• You’ve been given two urgent projects and told both are a top priority but only have time to handle one before the end of the day.
• A team member quit, you’ve been given their workload in addition to yours at the last minute, and you won’t be able to finish your most critical project while managing their workload.
• Your manager assigned you an urgent task, but before you’ve finished, asked you to support a different project that’s critical and needs more attention.

Common Interview Questions Regarding Prioritizing

The most common interview questions regarding prioritizing your work are:

• How do you prioritize your work?
• How do you manage your time and prioritize tasks?
• How do you handle multiple tasks and priorities?
• Tell me about a time you had to manage conflicting priorities at work

With interview questions 1, 2, and 3, you can answer in the same way, since they’re focused on your general approach to prioritizing tasks throughout your day.

Question 4 is a behavioral interview question. To answer this question, you’ll need to describe a specific time you had conflicting priorities. But the general idea for what to say to an employer is the same for all four questions:

The best way to answer any question about how you manage conflicting priorities or many tasks/deadlines is to show you stay calm and logical, and most importantly — have a system. If you can show employers you’ve faced competing priorities in the past and have a method for handling them, by giving an example or describing your system, you’ll satisfy the interviewer.

Below, I’ll share examples of how you’d answer interview questions about prioritizing your work.

How Do You Prioritize Your Work? Example Interview Answers

Example answer 1:.

I like to prioritize my work by keeping an Excel spreadsheet of my projects and their deadlines so I can see everything at a glance. Then, I sort and adjust the spreadsheet to prioritize my work based on the importance of a project, how long it’ll take, how urgent it is, and whether I’ll need input from other team members to complete the work. I review this sheet each morning. In my current role, I usually have six to eight projects. I’ve found that by communicating clearly with my team and manager at multiple points throughout each project and then tracking everything in Excel, I’m able to manage all of my tasks and hit each deadline, even if we hit some unexpected challenges or delays. I’ve found that communication is key in all of this, too.

Example Answer 2:

Working in retail customer service , it’s not always possible to handle everything urgent at once, so it’s important to know what the greatest priority is. For example, if a customer drops and breaks a glass jar in the aisle, cleaning that up is urgent because it’s a safety hazard. I’ve taken it upon myself to study and learn what’s most urgent in the job and if I’m not sure, I use my best judgment and then ask my manager after the fact if I acted correctly. Through this, I’m always learning and improving, and this helps me know the right priority to follow next time I’m faced with a similar situation.

Example Answer 3:

Each week, I look at my workload and projects and set a daily schedule to help me prioritize. Usually, I’ll estimate how long a project will take and give it a ranking in terms of how urgent it is based on that. Having a clear priority each day allows me to better manage my workload and juggle multiple tasks without missing deadlines. When necessary, I can prioritize within a day, to ensure that I’m tackling the most important jobs first thing in the morning.

Example Answer 4:

I’ve had to juggle multiple deadlines and projects in my two most recent jobs, so I developed a system that works well for me. I use a calendar and alert system to track my priority list so I can see what’s the most time-sensitive and urgent among my tasks. I also break each project into steps to see which pieces of the work are most urgent or require the most time. That way, I can set a priority each day for larger jobs or projects, which allows me to hit deadlines even on long, complex tasks. Some of the projects I’ve managed in my current role have lasted multiple months and involved 10-20 team members, for example.

Tell Me a Time When You Had to Manage Conflicting Priorities: Examples

In my senior year of college, I had three professors assign large, multi-week projects that were all due the same week. I knew this would be a crippling workload if I saved it for the last minute or didn’t prioritize and plan ahead, so I sat down, broke each project down into smaller tasks and estimated how long each smaller task would take. This showed me which tasks to tackle soonest, and I was able to use this system to get everything turned in on time. It worked, and I finished my senior year with a GPA of 3.8.

As a software developer , I’m typically working on two to three projects at a time, with varying importance and urgency. Each has a different project manager , too, so I’m often given conflicting tasks that are time-sensitive and important. For example, I was recently told by my manager to stop what I was doing and help out on an urgent task for the rest of my workday. He didn’t know that I had been pulled aside to work on another urgent project already, though, by a project manager in another group. So I simply said, “I’m happy to do that, but are you aware that I’ve been pulled in to work on <project name> by <project manager’s name>?” It turns out that my boss didn’t realize this, so this is an example of where clear communication and my ability to stay calm under pressure allowed me to determine the right priority and complete the most urgent tasks first.

Tailor Your Answer to the Company When Possible

Employers ask questions about prioritization because they want to see if you’d be able to prioritize effectively in their position. In most cases, you’ll have a variety of examples you can share for times you had to manage competing priorities. But you’ll impress the interviewer most if you can focus on examples and answers that are similar to the work you’d be doing in the job you’re discussing.

For example, imagine your last job was a mix of analytical skills and spreadsheet work, but also interpersonal interaction and teamwork. If this next job you’ve applied to is almost entirely teamwork-oriented and will have you interacting with clients/customers, then you’ll want to share examples of how you managed conflicting priorities when working with clients/customers. Showing you’ve been able to prioritize tasks in situations similar to the job you’re discussing will show the interviewer you’re a good fit for their company. However, if you’re now applying to positions involving more analytical work and solo work in spreadsheets and other tools, you’ll want to discuss how you manage individual work effectively instead.

So look at the job descriptions for positions you’re targeting and notice what important tasks they mention. Then think back to your past work and create a few examples that involve similar tasks and challenges where you used organization and time management to stay ahead and prioritize. That will give you better results in your job search.

If you’re able to talk about a time you went above and beyond what’s expected of you, that’s also great because it’s a soft skill that transfers into any new job. 

Mistakes to Avoid

There are a couple of mistakes you should avoid when telling the interviewer how you prioritize work. First, I recommend you avoid saying anything that will make it sound like you struggle to manage time or tasks. So your answer should highlight situations where anyone would have been overwhelmed or would have had to juggle multiple tasks. But if you sound like someone who is always falling behind or feeling overwhelmed at work, then that could cost you job offers .

One more mistake: I recommend leaving personal stories out of your answer.  It’s tempting to talk about work-life balance, and how you prioritize your work when also juggling raising children or any number of other personal and family obligations. However, your answer will be a lot simpler and less concerning to a potential employer if you focus on talking about how you approach each task at work. Talk about how you prioritize your work against the other tasks you’ve been assigned so you can hit deadlines. While that answer method is a bit dry/boring, it’ll avoid saying anything that’s a potential red flag to an employer, and that’s what you need to do when asked this question.

It’s not a question where you need to wow the interviewer. Instead, you’re looking to reassure them that you have a method for prioritization and can handle every task they give you.

For any type of job, employers want to know that you can prioritize work based on what’s urgent and create a workflow to stay focused on what’s needed. They also like to hear that you communicate with your team and management as a part of prioritizing when necessary. If you deliver an interview answer that sounds like the sample answers above, you’ll show employers you’re capable of being productive in their environment and of recognizing what’s urgent and important.

To wrap up, go create a few answers based on your own career experiences, especially those that will fit with the jobs you’re interviewing for. By practicing ahead of time, you’ll feel more confident and be ready to answer interview questions about how you prioritize work.

Related interview questions:

• How would you describe your work style?
• How do you handle conflict?
• Are you a leader or a follower?

About the Author

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Optimal priority assignment for real-time systems: a coevolution-based approach

• Open access
• Published: 06 August 2022
• Volume 27 , article number  142 , ( 2022 )

Cite this article

You have full access to this open access article

• Jaekwon Lee 1 , 2 ,
• Seung Yeob Shin   ORCID: orcid.org/0000-0001-9025-7173 1 ,
• Shiva Nejati 1 , 2 &
• Lionel C. Briand 1 , 2  

1 Altmetric

In real-time systems, priorities assigned to real-time tasks determine the order of task executions, by relying on an underlying task scheduling policy. Assigning optimal priority values to tasks is critical to allow the tasks to complete their executions while maximizing safety margins from their specified deadlines. This enables real-time systems to tolerate unexpected overheads in task executions and still meet their deadlines. In practice, priority assignments result from an interactive process between the development and testing teams. In this article, we propose an automated method that aims to identify the best possible priority assignments in real-time systems, accounting for multiple objectives regarding safety margins and engineering constraints. Our approach is based on a multi-objective, competitive coevolutionary algorithm mimicking the interactive priority assignment process between the development and testing teams. We evaluate our approach by applying it to six industrial systems from different domains and several synthetic systems. The results indicate that our approach significantly outperforms both our baselines, i.e., random search and sequential search, and solutions defined by practitioners. Our approach scales to complex industrial systems as an offline analysis method that attempts to find near-optimal solutions within acceptable time, i.e., less than 16 hours.

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1 Introduction

Mission-critical systems are found in many different application domains, such as aerospace, automotive, and healthcare domains. The success of such systems depends on both functional and temporal correctness. For functional correctness, systems are required to provide appropriate outputs in response to the corresponding stimuli. Regarding temporal correctness, systems are supposed to generate outputs within specified time constraints, often referred to as deadlines. The systems that have to comply with such deadlines are known as real-time systems (Liu 2000 ). Real-time systems typically run multiple tasks in parallel and rely on a real-time scheduling policy to decide which tasks should have access to processing cores, i.e., CPUs, at any given time.

While developing a real-time system, one of the most common problems that engineers face is the assignment of priorities to real-time tasks in order for the system to meet its deadlines. Based on priorities of real-time tasks, the system’s task scheduler determines a particular order for allocating real-time tasks to processing cores. Hence, a priority assignment that is poorly designed by engineers makes the system scheduler execute tasks in an order that is far from optimal. In addition, the system will likely violate its performance and time constraints, i.e., deadlines, if a poor priority assignment is used.

In real-time systems, the problem of optimally assigning priorities to tasks is important not only to avoid deadline misses but also to maximize safety margins from task deadlines and is subject to engineering constraints . Tasks may exceed their expected execution times due to unexpected interrupts. For example, it is infeasible to test an aerospace system exhaustively on the ground such that potential environmental uncertainties, e.g., those related to space radiations, are accounted for. Hence, engineers assign optimal priorities to tasks such that the remaining times from tasks’ completion times to their deadlines, i.e., safety margins, are maximized to cope with potential uncertainties. Furthermore, engineers typically have to account for additional engineering constraints, e.g., they assign higher priorities to critical tasks that must always meet their deadlines compared to the tasks that are less critical or non-critical.

A brute force approach to find an optimal priority assignment would have to examine all n ! distinct priority assignments, where n denotes the number of tasks. Furthermore, for a given priority assignment, schedulability analysis is, in general, known as a hard problem (Audsley 2001 ), which determines whether or not tasks will always complete their executions within their specified deadlines. Thus, optimizing priority assignments is also a hard problem because the space of all possible system states to explore in order to find optimal priority assignments is very large. Most of the prior works on optimizing priority assignments provide analytical methods (Fineberg and Serlin 1967 ; Leung and Whitehead 1982 ; Audsley 1991 ; Davis and Burns 2007 ; Chu and Burns 2008 ; Davis and Burns 2009 ; Davis and Bertogna 2012 ), which rely on well-defined system models and are very restrictive. For example, they assume that tasks are independent, i.e., tasks do not share resources (Davis et al. 2016 ; Zhao and Zeng 2017 ). Industrial systems, however, are typically not compatible with such (simple) system models. In addition, none of the existing work addresses the problem of optimizing priority assignments by simultaneously accounting for multiple objectives, such as safety margins and engineering constraints, as discussed above.

Search-based software engineering (SBSE) has been successfully applied in many application domains, including software testing (Wegener et al. 1997 ; Wegener and Grochtmann 1998 ; Lin et al. 2009 ; Arcuri et al. 2010 ; Shin et al. 2018 ), program repair (Weimer et al. 2009 ; Tan et al. 2016 ; Abdessalem et al. 2020 ), and self-adaptation (Andrade and Macêdo 2013 ; Chen et al. 2018 ; Shin et al. 2020 ), where the search spaces are very large. Despite the success of SBSE, engineering problems in real-time systems have received much less attention in the SBSE community. In the context of real-time systems, there exists limited work on finding stress test scenarios (Briand et al. 2005 ) and predicting worst-case execution times (Lee et al. 2020b ), which complements our work.

In practice, priority assignments result from an interactive process between the development and testing teams. While developing a real-time system, developers assign priorities to real-time tasks in the system and then testers stress the system to check whether or not the system meets its specified deadlines. If testers find a problematic condition under which any of the tasks violates its deadline, developers have to modify the priority assignment to address the problem. The back-and-forth between the development and testing teams continues until a priority assignment that does not lead to any deadline miss is found or the one that yields the least critical deadline misses is identified. The process is, however, not automated.

In this article, we use metaheuristic search algorithms to automate the process of assigning priorities to real-time tasks. To mimic the interactive back-and-forth between the development and testing teams, we use competitive coevolutionary algorithms (Luke 2013 ). Coevolutionary algorithms are a specialized class of evolutionary search algorithms. They simultaneously coevolve two populations (also called species) of (candidate) solutions for a given problem. They can be cooperative or competitive. Such competitive coevolution is similar to what happens in nature between predators and preys. For example, faster preys escape predators more easily, and hence they have a higher probability of generating offspring. This impacts the predators, because they need to evolve as well to become faster if they want to feed and survive (Meneghini et al. 2016 ). Hence, the two species, i.e., predators and preys, have coevolved competitively. We note that no species has the competing traits of predators and preys simultaneously as such species could not evolve to survive. In our context, priority assignments defined by developers can be seen as preys and stress test scenarios as predators. The priority assignments need to evolve so that stress testing is not able to push the system into breaking its real-time constraints. Dually, stress test scenarios should evolve to be able to break the system when there is a chance to do so.

Contributions.

We propose an O ptimal P riority A ssignment M ethod for real-time systems (OPAM). Specifically, we apply multi-objective, two-population competitive coevolution (Popovici et al. 2012 ) to address the problem of finding near-optimal priority assignments, aiming at maximizing the magnitude of safety margins from deadlines and constraint satisfaction. In OPAM, two species relate to priority assignment and stress testing coevolve synchronously, and compete against each other to find the best possible solutions. We evaluated OPAM by applying it to six complex, industrial systems from different domains, including the aerospace, automotive, and avionics domains, and several synthetic systems. Our results show that: (1) OPAM finds significantly better priority assignments compared to our baselines, i.e., random search and sequential search, (2) the execution time of OPAM scales linearly with the number of tasks in a system and the time required to simulate task executions, and (3) OPAM priority assignments significantly outperform those manually defined by engineers based on domain expertise.

We note that OPAM is the first attempt to apply coevolutionary algorithms to address the problem of priority assignment. Further, it enables engineers to explore trade-offs among different priority assignments with respect to two objectives: maximizing safety margins and satisfying engineering constraints. Our full evaluation package is available online (Lee et al. 2021 ).

Organization.

The remainder of this article is structured as follows: Section  2 motivates our work. Section  3 defines our specific problem of priority assignment in practical terms. Section  4 discusses related work. Sections  5 and  6 describe OPAM. Section  7 evaluates OPAM. Section  8 concludes this article.

2 Motivating case study

We motivate our work using an industrial case study from the satellite domain. Our case study concerns a mission-critical real-time satellite, named ESAIL (LuxSpace 2021 ), which has been developed by LuxSpace – a leading system integrator for microsatellites and aerospace system. ESAIL tracks vessels’ movements over the entire globe as the satellite orbits the earth. The vessel-tracking service provided by ESAIL requires real-time processing of messages received from vessels in order to ensure that their voyages are safe with the assistance of accurate, prompt route provisions. Also, as ESAIL orbits the planet, it must be oriented in the proper position on time in order to provide services correctly. Hence, ESAIL’s key operations, implemented as real-time tasks, need to be completed within acceptable times, i.e., deadlines.

Engineers at LuxSpace analyze the schedulability of ESAIL across different development stages. At an early design stage, the engineers use a priority assignment method that extends the rate monotonic scheduling policy (Fineberg and Serlin 1967 ), which is a theoretical priory assignment algorithm used in real-time systems. At a later development stage, if the engineers found that any real-time task of ESAIL cannot complete its execution within its deadline, the engineers, in our study context, reassign priorities to tasks in order to address the problem of deadline violations.

The rate monotonic policy assigns priorities to tasks that arrive to be executed periodically and must be completed within a certain amount of time, i.e., periodic tasks with hard deadlines. According to the policy, periodic tasks that arrive frequently have higher priorities than those of other tasks that arrive rarely. In ESAIL, for example, if the vessel-tracking task arrives every 100ms and the satellite-position control task arrives every 150ms, the former has a higher priority than the latter. However, the rate monotonic policy does not account for tasks that arrive irregularly and should be completed within a reasonable amount of time, i.e., aperiodic tasks with soft deadlines. ESAIL contains aperiodic tasks with soft deadlines as well, such as a task for updating software. Hence, the engineers extend the rate monotonic policy to assign priorities to all tasks of ESAIL. The extensions are as follows: First, the engineers assign priorities to periodic tasks based on the rate monotonic policy. Second, the engineers assign lower priorities to aperiodic tasks than those of periodic tasks. As aperiodic tasks with soft deadlines are typically considered less critical than periodic tasks with hard deadlines, the engineers aim to ensure that periodic tasks complete their executions within their deadlines by assigning lower priorities to aperiodic tasks while periodic tasks have higher priority. Engineers use a heuristic to assign priorities to aperiodic tasks. They treat aperiodic tasks as (pseudo-)periodic tasks by setting aperiodic tasks’ (expected) minimum arrival rates as their fixed arrival periods, making the tasks frequently arrive. The engineers then apply the rate monotonic policy for the aperiodic tasks with the synthetic periods while ensuring that aperiodic tasks have lower priorities than those of periodic tasks.

A priority assignment made at an early design stage keeps changing while developing ESAIL due to various reasons, such as changes in requirements and implementation constraints. At a development stage, instead of relying on the extended rate monotonic policy, the engineers assign priorities based on their domain expertise, manually inspecting schedulability analysis results. Hence, a priority assignment at later development stages often does not follow the extended rate monotonic policy. For example, as aperiodic tasks are also expected to be completed within a reasonable amount of time, some aperiodic tasks may have higher priorities than some periodic tasks as long as they are schedulable.

Engineers at LuxSpace, however, are still faced with the following issues: (1) Their priority assignment method, which extends the rate monotonic scheduling policy, assigns priorities to tasks in order to ensure only that tasks are to be schedulable. However, engineers have a pressing need to understand the quality of priority assignments in detail as they impact ESAIL operations differently. For example, once ESAIL is launched into orbit, the satellite operates in the space environment, which is inherently impossible to be fully tested on the ground. Unexpected space radiations may trigger unusual system interrupts, which hasn’t been observed on the ground, resulting in overruns of ESAIL tasks’ executions. In such cases, a priority assignment assessed on the ground may not be able to tolerate such unexpected uncertainties. Hence, engineers need a priority assignment that enables ESAIL tasks to tolerate unpredictable uncertainties as much as possible and to be schedulable. (2) Engineers at LuxSpace assign priorities to tasks without any systematic assistance. Instead, they rely on their expertise and the current practices described above to manually assign priorities to ensure that tasks are to be schedulable. To this end, we are collaborating with LuxSpace to develop a solution for addressing these issues in assigning task priority.

3 Problem description

This section defines the task, scheduler, and schedulability concepts, which extend the concepts defined in our previous work (Lee et al. 2020b ) by augmenting our previous definitions with the notions of safety margins, constraints in assigning priorities, and relationships between real-time tasks. We then describe the problem of optimizing priority assignments such that we maximize the magnitude of safety margins and the degree of constraint satisfaction. Figure  1 shows an overview of the conceptual model that represents the key abstractions required to analyze optimal priority assignments for real-time systems. The entities in the conceptual model are described below.

A conceptual model representing the key abstractions to analyze optimal priority assignments

We denote by j a real-time task that should complete its execution within a specified deadline after it is activated (or arrived). Every real-time task j has the following properties: priority denoted by p r ( j ), deadline denoted by d l ( j ), and worst-case execution time (WCET) denoted by w c e t ( j ). Task priority p r determines if an execution of a task is preempted by another task. Typically, a task j preempts the execution of a task \(j^{\prime }\) if the priority of j is higher than the priority of \(j^{\prime }\) , i.e., \({pr}(j) > {pr}(j^{\prime })\) . The p r ( j ) priority is a fixed value assigned to task j . Such fixed priorities are determined offline; hence, they are not changed online for any reason. Note that a real-time task scheduler that relies on fixed priorities is applied in all the study subjects in this article (see Section  7.2 ) and is commonly used in industrial systems (Briand et al. 2005 ; Guan et al. 2009 ; Lin et al. 2009 ; Anssi et al. 2011 ; Zeng et al. 2014 ; Di Alesio et al. 2015 ; Du̇rr et al. 2019 ; Lee et al. 2020a ).

The d l ( j ) function determines the deadline of a task j relative to its arrival time. A task deadline can be either hard or soft . A hard deadline of a task j constrains that j must complete its execution within a deadline d l ( j ) after j is activated. While violations of hard deadlines are not acceptable, depending on the operating context of a system, violating soft deadlines may be to some extent tolerated. Note that we use a metaheuristic search relying on fitness functions quantifying the degrees of deadline misses, safety margins, and constraint satisfaction. Such functions do not depend on the nature of the deadlines. Our approach outputs a set of priority assignments that are Pareto optimal with respect to safety margins and constraint satisfaction. Engineers then perform domain-specific trade-off analysis among Pareto solutions. Hence, in this article, we handle hard and soft deadline tasks in the same manner.

Real-time tasks are either periodic or aperiodic . Periodic tasks, which are typically triggered by timed events, are invoked at regular intervals specified by their period . We denote by p d ( j ) the period of a periodic task j , i.e., a fixed time interval between subsequent activations (or arrivals) of j . Any task that is not periodic is called aperiodic. Aperiodic tasks have irregular arrival times and are activated by external stimuli which occur irregularly. In real-time analysis, based on domain knowledge, we typically specify a minimum inter-arrival time denoted by p m i n ( j ) and a maximum inter-arrival time denoted by p m a x ( j ) indicating the minimum and maximum time intervals between two consecutive arrivals of an aperiodic task j . In real-time analysis, sporadic tasks are often separately defined as having irregular arrival intervals and hard deadlines (Liu 2000 ). In our conceptual definitions, however, we do not introduce new notations for sporadic tasks because the deadline and period concepts defined above sufficiently characterize sporadic tasks. Note that for periodic tasks j , we have p m i n ( j ) = p m a x ( j ) = p d ( j ). Otherwise, for aperiodic tasks j , we have p m a x ( j ) > p m i n ( j ).

Task relationships.

The execution of a task j depends not only on its own parameters described above, e.g., priority p r ( j ) and period p d ( j ), but also on its relationships with other tasks. Relationships between tasks are typically determined by task interactions related to accessing shared resources and triggering arrivals of other tasks (Di Alesio et al. 2012 ). Specifically, if two tasks j and \(j^{\prime }\) access a shared resource r in a mutually exclusive way, j may be blocked from executing for the period during which \(j^{\prime }\) accesses r . We denote by \({dp}(j,j^{\prime })\) the resource-dependency relation between tasks j and \(j^{\prime }\) that holds if j and \(j^{\prime }\) have mutually exclusive access to a shared resource r such that they cannot be executed in parallel or preempt each other, but one can execute only after the other has completed accessing r .

The other type of relationship between tasks is related to a task j triggering the arrival of another task \(j^{\prime }\) . This is a common interaction between tasks (Locke et al. 1990 ; Anssi et al. 2011 ; Di Alesio et al. 2015 ). For example, j may hand over some of its workload to \(j^{\prime }\) due to performance or reliability reasons. We denote by \({tr}(j,j^{\prime })\) the triggering relation between tasks j and \(j^{\prime }\) that holds if j triggers the arrival of \(j^{\prime }\) . We note that both relationships are defined at the level of tasks, following prior works (Locke et al. 1990 ; Anssi et al. 2011 ; Di Alesio et al. 2015 ) describing the five industrial case study systems used in our experiments (see Section  7.2 ).

Let J be a set of tasks to be scheduled by a real-time scheduler. A scheduler then dynamically schedules executions of tasks in J according to the tasks’ arrivals and the scheduler’s scheduling policy over the scheduling period \(\mathbb {T} = [0,\mathbf {T}]\) . We denote by a t k ( j ) the k th arrival time of a task j ∈ J . The first arrival of a periodic task j does not always occur immediately at the system start time (0). Such offset time from the system start time to the first arrival time a t 1 ( j ) of j is denoted by o f f s e t ( j ). For a periodic task j , the k th arrival of j within \(\mathbb {T}\) is a t k ( j ) ≤ T and is computed by a t k ( j ) = o f f s e t ( j ) + ( k − 1) ⋅ p d ( j ). For an aperiodic task \(j^{\prime }\) , \({at}_{k}(j^{\prime })\) is determined based on the k − 1th arrival time of \(j^{\prime }\) and its minimum and maximum arrival times. Specifically, for k > 1, \({at}_{k}(j^{\prime }) \in [{at}_{k-1}(j^{\prime })+{pmin}(j^{\prime }), {at}_{k-1}(j^{\prime })+{pmax}(j^{\prime })]\) and, for k = 1, \({at}_{1}(j^{\prime }) \in [{pmin}(j^{\prime }), {pmax}(j^{\prime })]\) , where \({at}_{k}(j^{\prime }) < \mathbf {T}\) .

A scheduler reacts to a task arrival at a t k ( j ) by scheduling the execution of j . Depending on a scheduling policy (e.g., rate monotonic scheduling policy for single-core systems (Fineberg and Serlin 1967 ) and single-queue multi-core scheduling policy (Arpaci-Dusseau and Arpaci-Dusseau 2018 )), an arrived task j may not start its execution at the same time as it arrives when higher priority tasks are executing on all processing cores. Also, task executions may be interrupted due to preemption. We denote by e t k ( j ) the completion time for the k th arrival of a task j . According to the worst-case execution time of a task j , we have: e t k ( j ) ≥ a t k ( j ) + w c e t ( j ).

During system operation, a scheduler generates a schedule scenario which describes a sequence of task arrivals and their completion time values. We define a schedule scenario as a set S of tuples ( j , a t k ( j ), e t k ( j )) indicating that a task j has arrived at a t k ( j ) and completed its execution at e t k ( j ). Due to a degree of randomness in task execution times and aperiodic task arrivals, a scheduler may generate a different schedule scenario for different runs of a system.

Figure  2 shows two schedule scenarios S (Figure  2a ) and \(S^{\prime }\) (Figure  2b ) produced by a scheduler over the [0,23] time period of a system run. Both S and \(S^{\prime }\) describe executions of three tasks, j 1 , j 2 , and j 3 arrived at the same time stamps (see a t i in the figures). In both scenarios, the aperiodic task j 1 is characterized by: p m i n ( j 1 ) = 5, p m a x ( j 1 ) = 13, d l ( j 1 ) = 4, and w c e t ( j 1 ) = 2. The aperiodic task j 2 is characterized by: p m i n ( j 2 ) = 3, p m a x ( j 2 ) = 10, d l ( j 2 ) = 4, and w c e t ( j 2 ) = 1. The periodic task j 3 is characterised by: p d ( j 3 ) = 8, d l ( j 3 ) = 7, and w c e t ( j 3 ) = 3. The priorities of the three tasks in S (resp. \(S^{\prime }\) ) satisfy the following: p r ( j 1 ) > p r ( j 2 ) > p r ( j 3 ) (resp. p r ( j 2 ) > p r ( j 3 ) > p r ( j 1 )). In both scenarios, task executions can be preempted depending on their priorities. Then, S is defined by S = {( j 1 ,5,7), …, ( j 2 ,4,5), …, ( j 3 ,8,14), ( j 3 ,16,19))}; and \(S^{\prime }\) is defined by \(S^{\prime } = \{(j_{1}, 5, 7)\) , …, ( j 2 ,4,5), …, ( j 3 ,8,12), ( j 3 ,16,19))}.

Example schedule scenarios S and \(S^{\prime }\) of three tasks: j 1 , j 2 , and j 3 . (a) The S schedule scenario is produced when p r ( j 1 ) = 3, p r ( j 2 ) = 2, and p r ( j 3 ) = 1. (b) The \(S^{\prime }\) schedule scenario is produced when p r ( j 1 ) = 1, p r ( j 2 ) = 3, and p r ( j 3 ) = 3

Schedulability.

Given a schedule scenario S , a task j is schedulable if j completes its execution before its deadline, i.e., for all e t k ( j ) observed in S , e t k ( j ) ≤ a t k ( j ) + d l ( j ). Let J be a set of tasks to be scheduled by a scheduler. A set J of tasks is then schedulable if for every schedule S of J , we have no task j ∈ J that misses its deadline.

As shown in schedule scenarios S and \(S^{\prime }\) presented in Fig.  2a and b, respectively, all three tasks, j 1 , j 2 , and j 3 , are schedulable. However, we note that the overall amounts of remaining time, i.e., safety margins, from the tasks’ completions to their deadlines observed in S and \(S^{\prime }\) are different (see the second completion times and deadlines of j 1 , j 2 , and j 3 in S and \(S^{\prime }\) ) because S and \(S^{\prime }\) are produced by using different priority assignments. Engineers typically desire to assign optimal priorities to real-time tasks that aim at maximizing such safety margins, as discussed below.

In real-time systems, fixed priorities are typically assigned to tasks (Davis et al. 2016 ; Lee et al. 2020a ). Finding an appropriate priority assignment is important not only for ensuring the schedulability of a system but also for maximizing the safety margins within which a system can tolerate unexpected execution time overheads. For example, if an unpredictable error occurs and triggers check-point mechanisms (Davis and Burns 2007 ), which re-execute part or all of a task j , then the execution time of j unexpectedly overruns. Hence, engineers need an optimal priority assignment that maximizes the overall remaining times from task completion times to task deadlines, i.e., safety margins.

While assigning priorities to tasks, engineers also account for constraints, that are often but not always domain-specific. For example, aperiodic tasks’ priorities should be lower than those of periodic tasks because periodic tasks are often more critical than aperiodic tasks. Hence, engineers develop a system that prioritizes executions of periodic tasks over aperiodic tasks. Recall from Section  2 , this constraint is desirable by engineers. When needed, however, engineers can violate the constraint to some extent in order to ensure that aperiodic tasks complete within a reasonable amount of time while periodic tasks meet their deadlines. Constraints can be either hard constraints, which must be satisfied, or soft constraints, which are desired to be satisfied. In our study, hard constraints need to be assured while scheduling tasks, e.g., a running task’s priority must be higher than a ready task’s priority, which are enforced by a scheduler. In the context of optimizing priority assignments, we focus on maximizing the extent of satisfying soft constraints. We refer to a soft constraint as a constraint in this paper.

Our work aims at optimizing priority assignments that maximize the safety margins while satisfying such constraints. Specifically, for a set J of tasks to be analyzed, we define three concepts as follows: (1) a priority assignment for J denoted by \(\rightarrow {P} \) , (2) the magnitude of safety margins for a priority assignment \(\rightarrow {P}\) denoted by \({fs}(\rightarrow {P})\) , and (3) the degree of constraint satisfaction denoted by \({fc}(\rightarrow {P})\) . We note that Section  6.3 describes how we optimize \(\rightarrow {P}\) , and compute \({fs}(\rightarrow {P})\) and \({fc}(\rightarrow {P})\) in detail. Our study aims at finding a set B of best possible priory assignments that are Pareto optimal (Knowles and Corne 2000 ) such that a priority assignment \(\rightarrow {P} \in \mathbf {B}\) maximizes both \({fs}(\rightarrow {P})\) and \({fc}(\rightarrow {P})\) , and any other priority assignments in B are equally viable.

4 Related Work

This section discusses related research strands in the areas of priority assignments, real-time analysis using exhaustive techniques, search-based analysis in real-time systems, and coevolutionary analysis in software engineering.

Priority assignment.

The problem of optimally assigning priorities to real-time tasks has been widely studied (Fineberg and Serlin 1967 ; Liu and Layland 1973 ; Leung and Whitehead 1982 ; Audsley 1991 ; Tindell et al. 1994 ; George et al. 1996 ; Audsley 2001 ; Davis and Burns 2007 ; Chu and Burns 2008 ; Davis and Burns 2009 ; 2011 ; Davis and Bertogna 2012 ; Davis et al. 2016 ; Zhao and Zeng 2017 ; Hatvani et al. 2018 ). Fineberg and Serlin ( 1967 ) reported early work that relies on a simple system model, assuming, for example, that all tasks arrive periodically, tasks run on a single processing core, tasks’ deadlines are equal to their periods, and task executions are independent from one another. They proposed a priority assignment method, named rate-monotonic priority ordering (RMPO), that assigns higher priorities to the tasks with shorter periods. RMPO can find a feasible priority assignment that guarantees periodic tasks to be schedulable when such priority assignments exist (Liu and Layland 1973 ). Leung and Whitehead ( 1982 ) extended RMPO to relax one of the underlying assumptions made in RMPO. Specifically, their priority assignment approach, known as deadline-monotonic priority ordering (DMPO), accounts for task deadlines that can be less than or equal to their periods. In contrast to our work, however, these methods are often not applicable to industrial systems that are not compatible with their simplified system models. Recall from Section  3 that a realistic system typically consists of both periodic and aperiodic tasks. Task executions depend on their relationships, i.e., resource dependencies and triggering relationships, with other tasks.

Audsley ( 2001 ) designed a priority assignment method, named optimal priority assignment (OPA), that relies on an existing schedulability analysis method M . OPA guarantees to find a feasible priority assignment that is schedulable according to M if such priority assignments exist. OPA is applicable to more complex systems than those supported by the methods mentioned above, i.e., RMPO and DMPO. Specifically, OPA can find a feasible priority assignment even in the following situations: (1) First arrivals of periodic tasks occur after some offset time (Audsley 1991 ). (2) Aperiodic tasks have arbitrary deadlines (Tindell et al. 1994 ). (3) Task executions are scheduled based on a non-preemptive scheduling policy (George et al. 1996 ). (4) Tasks run on multiple processing cores (Davis and Burns 2011 ). Unlike our approach that accounts for two objectives, safety margins and engineering constraints (see Section  3 ), OPA attempts to find a feasible priority assignment whose only objective is to make all tasks schedulable. Note that such a feasible priority assignment does not necessarily maximize safety margins as discussed in Section  3 . Hence, a feasible priority assignment obtained by OPA is often fragile and sensitive any changes in task executions and unable to accommodate unexpected overheads in task execution times, which are commonly observed in industrial systems (Davis and Burns 2007 ).

OPA has been extended by several works (Davis and Burns 2007 ; Chu and Burns 2008 ; Davis and Burns 2009 ; Davis and Bertogna 2012 ). Davis and Burns ( 2007 ) presented a robust priority assignment method (RPA) with a degree of tolerance for unexpected overruns of task execution times. Chu and Burns ( 2008 ) introduced an extended OPA algorithm (OPA-MLD) that minimizes the lexicographical distance between the desired priority assignment and the one obtained by the algorithm. OPA-MLD enables important tasks to have higher priorities. Davis and Bertogna ( 2012 ) proposed an RPA extension (FNR-PA) to make RPA work when a system allows task preemption to be deferred for some interval of time. Davis and Burns ( 2009 ) developed a probabilistic robust priority assignment method (PRPA) for a real-time system to be less likely to violate its deadlines. Even though the prior works mentioned above improve OPA to some extent, they assume that task executions are independent of one another. In contrast to these existing approaches, OPAM accounts for dependencies among task executions, i.e., resource dependencies and triggering relationships (see our problem description in Section  3 ).

Some recent priority assignment techniques address scalability. Hatvani et al. ( 2018 ) presented an optimal priority and preemption-threshold assignment algorithm (OPTA) that attempts to decrease the computation time for finding a feasible priority assignment. OPTA uses a heuristic to traverse a problem space while pruning infeasible paths to efficiently and effectively explore the problem space. Zhao and Zeng ( 2017 ) introduced an effective priority assignment framework (EPAF) that combines a commercial solver for integer linear programs and their problem-specific optimization algorithm. However, these methods rely on simple system models that assume, for example, task executions to be independent and running on a single processing core. Therefore, the applicability of these techniques is limited. In contrast, recall from Sections  2 and  3 that our approach aims at scaling to complex industrial systems while accounting for realistic system characteristics regarding task periods, inter-arrival times, resource dependencies, triggering relationships, and multiple processing cores.

Table  1 compares our work, OPAM, with the other priority assignment techniques mentioned above. As shown in the table, we note that prior works rely on system models that are very restrictive. In particular, existing work assumes that task executions are independent of one another. However, task dependencies such as resource dependencies and triggering relationships are commonly observed in industrial systems. In addition, we note that no existing solution simultaneously accounts for safety margins and engineering constraints. Hence, to our knowledge, OPAM is the first attempt to provide engineers with a set of equally viable priority assignments, allowing trade-off analysis with respect to the two objectives: maximizing safety margins and satisfying engineering constraints.

Real-time analysis using exhaustive techniques.

Constraint programming and model checking have been applied to conclusively and exhaustively verify whether or not a system meets its deadlines (Kwiatkowska et al. 2011 ; Di Alesio et al. 2012 ; Nejati et al. 2012 ; Di Alesio et al. 2013 ). Existing research on priority assignment based on OPA rely on such exhaustive techniques to prove the schedulability of a set of tasks for a given priority assignment. We note that schedulability analysis is, in general, an NP-hard problem (Davis et al. 2016 ) that cannot be solved in polynomial time. As a result, exhaustive techniques based on model checking and constraint solving are often not amenable to analyze large industrial systems such as ESAIL – our motivating case study system – described in Section  2 . To assess if exhaustive techniques could scale to ESAIL, as discussed in Section  7.8 , we performed a preliminary experiment using UPPAAL (Behrmann et al. 2004 ), a model checker for real-time systems. We observed that UPPAAL was not able to verify schedulability of ESAIL tasks for a fixed priority assignment even after letting it run for several days (see Section  7.8 for more details).

Search-based analysis in real-time systems.

In real-time systems, most of the existing works that use search-based techniques focus on testing (Wegener et al. 1997 ; Wegener and Grochtmann 1998 ; Briand et al. 2005 ; Lin et al. 2009 ; Arcuri et al. 2010 ). Wegener et al. ( 1997 , 1998 ) introduced a testing approach based on a genetic algorithm that aims to check computation time, memory usage, and task synchronization by analyzing the control flow of a program. Briand et al. ( 2005 ) applied a genetic algorithm to find stress test scenarios for real-time systems. Lin et al. ( 2009 ) proposed a search-based approach to check whether a real-time system meets its timing and security constraints. Arcuri et al. ( 2010 ) presented a black-box system testing approach based on a genetic algorithm. Beyond testing real-time systems, Nejati et al. ( 2013 , 2014 ) developed a search-based trade-off analysis technique that helps engineers balance the satisfaction of temporal constraints and keeping the CPU time usage at an acceptable level. Lee et al. ( 2020b ) combined a search algorithm and machine learning to estimate safe ranges of worst-case task execution times within which tasks likely meet their deadlines. In contrast to these prior works, OPAM addresses the problem of optimally assigning priorities to real-time tasks while accounting for multiple objectives regarding safety margins and engineering constraints, thus enabling Pareto (trade-off) analysis. Further, OPAM uses a multi-objective, competitive coevolutionary search algorithm, which has been rarely applied to date in prior studies of real-time systems, as discussed next.

Coevolutionary analysis in software engineering.

Despite the success of search-based software engineering (SBSE) in many application domains including software testing (Wegener et al. 1997 ; Wegener and Grochtmann 1998 ; Lin et al. 2009 ; Arcuri et al. 2010 ; Shin et al. 2018 ), program repair (Weimer et al. 2009 ; Tan et al. 2016 ; Abdessalem et al. 2020 ), and self-adaptation (Andrade and Macêdo 2013 ; Chen et al. 2018 ; Shin et al. 2020 ), coevolutionary algorithms have been applied in only a few prior studies (Wilkerson and Tauritz 2010 ; Wilkerson et al. 2012 ; Boussaa et al. 2013 ). Wilkerson et al. ( 2010 , 2012 ) present a coevolution-based approach to automatically correct software. Their work introduced a program representation language to facilitate their automated corrections. (Boussaa et al. 2013 ) developed a code-smells detection approach. The main idea is to evolve two competing populations of code-smell detection rules and artificial code-smells. Unlike these prior works, we study the problem of optimally assigning priorities to tasks in real-time systems. To our knowledge, we are the first to address the priority assignment problem using a multi-objective, competitive coevolutionary search algorithm.

5 Approach Overview

Finding an optimal priority assignment is an inherently interactive process. In practice, once engineers assign priorities to the real-time tasks in a system, testers then stress the system to find a condition, i.e., a particular sequence of task arrivals, in which a task execution violates its deadline. Testers typically use a simulator or hardware equipment to stress the system by triggering plausible worst-case arrivals of tasks that maximize the likelihood of deadline misses. If testers find task arrivals that induce deadline misses, the task arrivals are reported to engineers in order to fix the problem by reassigning priorities. This interactive process of assigning priorities and testing schedulability continues until both engineers and testers ensure that the tasks meet their deadlines.

For such intrinsically interactive problem-solving domains, we conjecture that coevolutionary algorithms are potentially suitable solutions. A coevolutionary algorithm is a search algorithm that mutually adapts one of different species, e.g., in our study, two populations of priority assignments and task-arrival sequences, acting as foils against one another. Specifically, we apply multi-objective, two-population competitive coevolution (Luke 2013 ) to address our problem of finding optimal priority assignments (see Section  3 ). In our approach, the two populations of priority assignments and stress test scenarios, i.e., task-arrival sequences, evolve synchronously, competing with each other in order to search for optimal priority assignments that maximize the magnitude of safety margins from deadlines and the extent of constraint satisfaction. Note that better priority assignments enable a system to achieve larger safety margins. Hence, those priority assignments have a higher chance to pass stress test scenarios. This impacts the stress test scenarios because they need to evolve as well, aiming at inducing deadline misses in the system.

Recall from Section  4 that most of the existing SBSE research relies on search algorithms using a single population (Chen et al. 2018 ; Abdessalem et al. 2020 ; Shin et al. 2020 ). However, such algorithms do not fit the problem of priority assignments targeted here. When (1) two competing traits between task arrivals and priority assignments are encoded together in an individual of a single population and (2) two contradicting fitness functions regarding safety margins and deadline misses, which are exact opposites, assess such individuals, the notion of Pareto optimality is not applicable. In that case, maximizing the magnitude of safety margins necessarily entails minimizing the magnitude of deadline misses. Hence, a single population-based search algorithm cannot make Pareto improvements that maximize safety margins (resp. deadline misses) while not minimizing deadline misses (resp. safety margins). Specifically, the dominance relation over such individuals does not exist because if an individual I is strictly better than another individual \(I^{\prime }\) in one fitness value, I is always worse than \(I^{\prime }\) in the other fitness value. Hence, we are not able to obtain equally viable solutions with respect to the contradicting objectives using such a method.

Figure  3 shows an overview of our proposed solution: O ptimal P riority A ssignment M ethod for real-time tasks (OPAM). OPAM requires as input task descriptions defined by engineers, which specify task characteristics and their relationships (see Section  3 ). Given such input task descriptions, the “find worst task arrivals’ and “find best priority assignments” steps aim at generating worst-case sequences of task arrivals and best-case priority assignments, respectively. A worst-case sequence of task arrivals means that the magnitude of deadline misses, i.e., the amounts of time from task deadlines to task completion times, is maximized when tasks arrive as defined in the sequence. Note that if there is no deadline miss, a task-arrival sequence is considered worst-case if tasks complete their executions as close to their deadlines as possible. In contrast, a priority assignment is best-case when the magnitude of safety margins is maximized. Beyond maximizing safety margins, the “find best priority assignments” step accounts for satisfying engineering constraints in assigning priorities to tasks. OPAM evolves two competing populations of task-arrival sequences and priority assignments synchronously generated from the two steps. OPAM then outputs a set of priority assignments that are Pareto optimal with regards to the magnitude of safety margins and the extent of satisfying constraints. Hence, OPAM allows engineers to perform domain-specific trade-off analysis among Pareto solutions and is useful in practice to support decision making with respect to their task design. For example, suppose engineers develop a weakly hard real-time systems (Bernat and Burns 2001 ) that can tolerate occasional deadline misses. In that case, engineers may consider a few deadline misses as less important (as long as their consequences are negligible) than the overall magnitude of safety margins in their trade-off analysis. Section  6 describes OPAM in detail.

An overview of our O ptimal P riority A ssignment M ethod for real-time systems (OPAM)

6 Competitive Coevolution

Figure  4 describes the OPAM algorithm for finding optimal priority assignments, which employs multi-objective, two-population competitive coevolution. The algorithm first randomly initializes two populations A and P for task-arrival sequences and priority assignments, respectively (lines 13–15). For A , OPAM randomly varies task arrivals of aperiodic tasks to create p s a task-arrival sequences, according to the input task descriptions D . Regarding P , OPAM randomly creates p s p priority assignments that may include one defined by engineers if available.

Multi-objective two-population competitive coevolution for finding optimal priority assignments

The two populations sequentially evolve during the allotted analysis budget (see line 17 in Figure  4 ). The best priority assignment is the one that makes tasks schedulable and maximizes the magnitude of safety margins, while satisfying engineering constraints for a given worst sequence of task arrivals. Hence, searching for the best priority assignments involves searching for the worst sequences of task arrivals. We create two populations A and P searching for the worst arrival sequences and the best priority assignments, respectively. The fitness values of task-arrival sequences in A are computed based on how well they challenge the priority assignments in P , i.e., maximizing the magnitude of deadline misses (line 20). Likewise, the priority assignments in P are evaluated based on how well they perform against the task-arrival sequences in A , i.e., maximizing the magnitude of safety margins while satisfying constraints (line 25). Once the two populations are assessed against each other, OPAM generates the next populations based on the computed fitness values (lines 21 and 26). OPAM tailors the breading mechanisms of steady-state genetic algorithms (GA) (Whitley and Kauth 1988 ) for A and NSGAII (Deb et al. 2002 ) for P .

OPAM uses two types of fitness functions, namely internal and external fitness evaluations, which play a different and complementary role as described below. The two internal fitness evaluations in lines 20 and 25 of the listing in Figure  4 aim at selecting individuals – task-arrival sequences and priority assignments – for breeding the next A and P populations. OPAM evaluates the external fitness for the P population of priority assignments to find a best Pareto front (lines 28–31). As shown in lines 20 and 25, the internal fitness values of individuals in A (resp. P ) are computed based on how they perform with respect to individuals in P (resp. A ). Hence, an individual’s internal fitness is assessed through interactions with competing individuals. For example, a priority assignment in the first generation may have acceptable fitness values regarding safety margins and constraint satisfaction with respect to the first generation of task-arrival sequences, which are likely far from worst-case sequences. However, priority assignment fitness may get worse in later generations as the task-arrival sequences evolve towards larger deadline misses. Thus, if OPAM simply monitors internal fitness, it cannot reliably detect coevolutionary progress as an individual’s internal fitness changes according to competing individuals. The problem of monitoring progress in coevolution has been observed in many studies (Ficici 2004 ; Popovici et al. 2012 ). To address it, OPAM computes external fitness values of priority assignments in P based on a set E of task-arrival sequences generated independently from the coevolution process. By doing so, OPAM can observe the monotonic improvement of external fitness for priority assignments. We note that, in general, if interactions between two competing populations are finite and any interaction can be examined with non-zero probability at any time, monotonicity guarantees that a coevolutionary algorithm converges to a solution (Popovici et al. 2012 ).

We note that our approach for evolving task-arrival sequences is based on past work (Briand et al. 2005 ), where a specific genetic algorithm configuration was proposed to find worst-case task-arrival sequences. One significant modification is that OPAM accounts for task relationships – resource-dependency and task triggering relationships – and a multi-core scheduling policy based on simulations to evaluate the magnitude of deadline misses.

Following standard practice (Ralph et al. 2020 ), the next sections describe OPAM in detail by defining the representations, the scheduler, the fitness functions, and the evolutionary algorithms for coevolving the task-arrival sequences and priority assignments. We then describe the external fitness evaluation of OPAM.

6.1 Representations

OPAM coevolves two populations of task-arrival sequences and priority assignments. A task-arrival sequence is defined by their inter-arrival time characteristics (see Section  3 ). A priority assignment is defined by a function that maps priorities to tasks.

Task-arrival sequences.

Given a set J of tasks to be scheduled, a feasible sequence of task arrivals is a set A of tuples ( j , a t k ( j )) where j ∈ J and a t k ( j ) is the k th arrival time of a task j . Thus, a solution A represents a valid sequence of task arrivals of J (see valid a t k ( j ) computation in Section  3 ). Let \(\mathbb {T} = [0, \mathbf {T}]\) be the time period during which a scheduler receives task arrivals. The size of A is equal to the number of task arrivals over the \(\mathbb {T}\) time period. Due to the varying inter-arrival times of aperiodic tasks (Section  3 ), the size of A will vary across different sequences.

Priority assignments.

Given a set J of tasks to be scheduled, a feasible priority assignment is a list \(\rightarrow {P}\) of priority p r ( j ) for each task j ∈ J . OPAM assigns a non-negative integer to a priority p r ( j ) of j such that priorities are comparable to one another. The size of \(\rightarrow {P}\) is equal to the number of tasks in J . Each task in J has a unique priority. Hence, a priority assignment \(\rightarrow {P}\) is a permutation of all tasks’ priorities. We note that these characteristics of priority assignments are common in many real-time analysis methods (Audsley 2001 ; Davis and Burns 2007 ; Zhao and Zeng 2017 ) and industrial systems (e.g., see our six industrial case study systems described in Section  7.2 ).

6.2 Simulation

OPAM relies on simulation for analyzing the schedulability of tasks in a scalable way. For instance, an inter-arrival time of a software update task in a satellite system is approximately at most three months. In such cases, conducting an analysis based on an actual scheduler is prohibitively expensive. Also, applying an exhaustive technique for schedulability analysis typically doesn’t scale to an industrial system (e.g., see our experiment results using a model checker described in Section  7.8 ). Instead, OPAM uses a real-time task scheduling simulator, named OPAMScheduler, which applies a scheduling policy, i.e., single-queue multi-core scheduling policy (Arpaci-Dusseau and Arpaci-Dusseau 2018 ), based on discrete simulation time events. Note that we chose the single-queue multi-core scheduling policy for OPAMScheduler since our case study systems (described in Section  7.2 ) rely on this policy.

OPAMScheduler takes as input a feasible task-arrival sequence A and a priority assignment \(\rightarrow {P}\) for scheduling a set J of tasks. It then outputs a schedule scenario as a set S of tuples ( j , a t k ( j ), e t k ( j )) where a t k ( j ) and e t k ( j ) are the k th arrival and end time values of a task j , respectively (see Section  3 ). For each task j , OPAMScheduler computes e t k ( j ) based on its WCET and scheduling policy while accounting for task relationships (see the \({dp}(j,j^{\prime })\) resource-dependency relationship and the \({tr}(j,j^{\prime })\) task triggering relationship in Section  3 ). To simulate the worst-case executions of tasks, OPAMScheduler assigns tasks’ WCETs to their execution times.

OPAMScheduler implements a single-queue multi-core scheduling policy (Arpaci-Dusseau and Arpaci-Dusseau 2018 ), which schedules a task j with explicit priority p r ( j ) and deadline d l ( j ). When tasks arrive, OPAMScheduler puts them into a single queue that contains tasks to be scheduled. At any simulation time, if there are tasks in the queue and multiple cores are available to execute tasks, OPAMScheduler first fetches a task j from the queue in which j has the highest priority p r ( j ). OPAMScheduler then allocates task j to any available core. Note that if task j shares a resource with a running task \(j^{\prime }\) in another core, i.e., the \({dp}(j,j^{\prime })\) resource-dependency relationship holds, j will be blocked until \(j^{\prime }\) releases the shared resource.

OPAMScheduler works under the assumption that context switching time is negligible, which is also a working assumption in many scheduling analysis methods (Liu and Layland 1973 ; Audsley 2001 ; Di Alesio et al. 2015 ). Note that the assumption is practically valid and useful at an early development step in the context of real-time analysis. For instance, our collaborating partner, LuxSpace, accounts for the waiting time of tasks due to context switching between tasks through adding some extra time to WCET estimates at the task design stage. Note that OPAM can be applied with any scheduling policy, including those that account for context switching time and multiple queues.

6.3 Fitness functions

Internal fitness: deadline misses..

Given a feasible task-arrival sequence A and a priority assignment \(\rightarrow {P}\) , we formulate a function, \({fd}(A,\rightarrow {P})\) , to quantify the degree of deadline misses regarding a set J of tasks to be scheduled. To compute \({fd}(A,\rightarrow {P})\) , OPAM runs OPAMScheduler for A and \(\rightarrow {P}\) and obtains a schedule scenario S . We denote by d i s t k ( j ) the distance between the end time and the deadline of the k th arrival of task j observed in S and define d i s t k ( j ) = e t k ( j ) − a t k ( j ) + d l ( j ) (see Section  3 for the notation end time e t k ( a ), arrival time a t k ( j ), and deadline d l ( j )). We denote by l k ( j ) the last arrival index of a task j in A . Given a set J of tasks to be scheduled, the \({fd}(A,\rightarrow {P})\) function is defined as follows:

Note that \({fd}(A,\rightarrow {P})\) is defined as an exponential equation. Hence, when all task executions observed in a schedule scenario S meet their deadlines, \({fd}(A,\rightarrow {P})\) is a small value as any distance d i s t k ( j ) between the task end time and the deadline of the k th arrival of task j is a negative value. In contrast, deadline misses result in positive values for d i s t k ( j ). In such cases, \({fd}(A,\rightarrow {P})\) is a large value. The exponential form of \({fd}(A,\rightarrow {P})\) was precisely selected for this reason, to assign large values for deadline misses but small values when deadlines are met. By doing so, \({fd}(A,\rightarrow {P})\) prevents an undesirable solution that would result into many task executions meeting deadlines obfuscating a smaller number of deadline misses.

Following the principles of competitive coevolution, individuals in a population A of task-arrival sequences need to be assessed by pitting them against individuals in the other population P of priority assignments. We denote by f d ( A , P ) the internal fitness function that quantifies the overall magnitude of deadline misses across all priority assignment \(\rightarrow {P} \in \mathbf {P}\) , regarding a set J of tasks to be scheduled. The f d ( A , P ) fitness is used for breeding the next population of task-arrival sequences. OPAM aims to maximize f d ( A , P ), defined as follows:

Internal fitness: safety margins.

Given a feasible priority assignment \(\rightarrow {P}\) and a task-arrival sequence A , we denote by \({fs}(\rightarrow {P},A)\) the magnitude of safety margins regarding a set J of tasks to be scheduled. The computation of \({fs}(\rightarrow {P},A)\) is similar to the computation of \({fd}(A,\rightarrow {P})\) regarding the use of OPAMScheduler, which outputs a schedule scenario S . The difference is that OPAM reverses the sign of \({fd}(A,\rightarrow {P})\) as OPAM aims at maximizing the magnitude of safety margins. Given a set J of tasks to be scheduled, the \({fs}(\rightarrow {P},A)\) function is defined as follows:

Given two populations P and A of priority assignments and task-arrival sequences, similar to internal fitness f d ( A , P ), priority assignments in P need to be assessed against task-arrival sequences in A . We formulate an internal fitness function, \({fs}(\rightarrow {P},\mathbf {A})\) , to quantify the overall magnitude of safety margins across all task-arrival sequences A ∈ A , regarding a set J of tasks to be scheduled and a priority assignment \(\rightarrow {P}\) . OPAM relies on the \({fs}(\rightarrow {P},\mathbf {A})\) function to breed the next population of priority assignments. OPAM aims to maximize \({fs}(\rightarrow {P},\mathbf {A})\) , which is defined as follows:

Internal fitness: constraints.

Given a priority assignment \(\rightarrow {P}\) , we formulate an internal fitness function, \({fc}(\rightarrow {P})\) , to quantify the degree of satisfaction of soft constraints set by engineers. Such function is required as we recast the satisfaction of such constraints into an optimization problem, in order to minimize constraint violations. Specifically, OPAM accounts for the following constraint: aperiodic tasks should have lower priorities than those of periodic tasks. Recall from Section  2 that engineers consider this constraint to be desirable. We denote by \({lp}(\rightarrow {P})\) the lowest priority of periodic tasks in \(\rightarrow {P}\) . For a set J of tasks to be scheduled, OPAM aims to maximize \({fc}(\rightarrow {P})\) , which is defined as follows:

Greater p r ( j ) values denote higher priorities. Given a priority assignment \(\rightarrow {P}\) , if p r ( j ) for an aperiodic task j is lower than the priority of any of the periodic tasks, \({lp}(\rightarrow {P}) - {pr}(j)\) is a positive value. OPAM measures the difference between priorities of aperiodic and periodic tasks. By doing so, \({fc}(\rightarrow {P})\) rewards aperiodic tasks that satisfy the above constraint and consistently penalizes those that violate it. Hence, OPAM aims at maximizing \({fc}(\rightarrow {P})\) .

External fitness: safety margins and constraints.

To examine the quality of priority assignments and monitor the progress of coevolution, OPAM takes as input a set E of task-arrival sequences created independently from the coevolution process. Given a set E of task-arrival sequences and a priority assignment \(\rightarrow {P}\) , OPAM utilizes \({fs}(\rightarrow {P},\mathbf {E})\) and \({fc}(\rightarrow {P})\) described above as external fitness functions for quantifying the magnitude of safety margins and the extent of constraint satisfaction, respectively. As E does not change over the coevolution process, \({fs}(\rightarrow {P},\mathbf {E})\) is used for evaluating a priority assignment \(\rightarrow {P}\) since it is not impacted by the evolution of task-arrival sequences. Hence, external fitness functions ensure that OPAM monitors the progress of coevolution in a stable manner. Given two populations P and A of priority assignments and task-arrival sequences, we recall that the f d ( A , P ) internal fitness function quantifies the overall magnitude of deadline misses across all priority assignments in P for the given sequence of task arrivals A . The \({fs}(\rightarrow {P},\mathbf {A})\) internal fitness function quantifies the overall magnitude of safety margins across all sequences of task arrivals in A for the given priority assignments \(\rightarrow {P}\) . Hence, the internal fitness of A (resp. \(\rightarrow {P}\) ) is assessed through interactions with competing individuals in P (resp. A ). Therefore, if OPAM relies only on the internal fitness functions, it cannot gauge the progress of coevolution in a stable manner as an individual’s internal fitness depends on competing individuals.

We note that soft deadline tasks also require to execute within reasonable execution time, i.e., (soft) deadline. As the above fitness functions return quantified degrees of deadline misses and safety margins, OPAM uses the same fitness functions for both soft and hard deadline tasks.

6.4 Evolution: Worst-case task arrivals

The algorithm in Figure  5 describes in detail the evolution of task-arrival sequences in lines 18–21 of the listing in Figure  4 . OPAM adapts a steady-state Genetic Algorithm (GA) (Luke 2013 ) for evolving task-arrival sequences. As shown in lines 8–14, OPAM first evaluates each task-arrival sequence in the A population against the P population of priority assignments. OPAM executes OPAMScheduler to obtain a schedule scenario S for a task-arrival sequence A i ∈ A and a priority assignment \(\rightarrow {P}_{l} \in \mathbf {P}\) (line 11). OPAM then computes the internal fitness f d ( A i , P ) capturing the magnitude of deadline misses (lines 12–14). We note that a steady-state GA iteratively breeds offspring, assess their fitness, and then reintroduce them into a population. However, OPAM computes internal fitness of all task-arrival sequences in A at every generation. This is because internal fitness is computed in relation to P , which is coevolving with A .

A steady-state GA-based algorithm for evolving task-arrival sequences

Breeding the next population is done by using the following genetic operators: (1) Selection: OPAM selects candidate task-arrival sequences using a tournament selection technique, with the tournament size equal to two which is the most common setting (Gendreau and Potvin 2010 ) (line 17 in Figure  5 ). (2) Crossover: Selected candidate task-arrival sequences serve as parents to create offspring using a crossover operation (line 18). (3) Mutation: The offspring are then mutated (line 19). Below, we describe our crossover and mutation operators. Crossover. A crossover operator is used to produce offspring by mixing traits of parent solutions. OPAM modifies the standard one-point crossover operator (Luke 2013 ) as two parent task-arrival sequences A p and A q may have different sizes, i.e., | A p |≠| A q |. Let J = { j 1 , j 2 ,…, j m } be a set of tasks to be scheduled. Our crossover operator first randomly selects an aperiodic task j r ∈ J . For all i ∈ [1, r ] and j i ∈ J , OPAM then swaps all j i arrivals between the two task-arrival sequences A p and A q . Since J is fixed for all solutions, OPAM can cross over two solutions that may have different sizes. Mutation operator OPAM uses a heuristic mutation algorithm. For a task-arrival sequence A , OPAM mutates the k th task arrival time a t k ( j ) of an aperiodic task j with a mutation probability. OPAM chooses a new arrival time value of a t k ( j ) based on the [ p m i n ( j ), p m a x ( j )] inter-arrival time range of j . If such a mutation of the k th arrival time of j does not affect the validity of the k + 1th arrival time of j , the mutation operation ends. Specifically, let d be a mutated value of a t k ( j ). In case a t k + 1 ( j ) ∈ [ d + p m i n ( j ), d + p m a x ( j )], OPAM returns the mutated A task-arrival sequence.

After mutating the k th arrival time a t k ( j ) of a task j in a solution A , if the k + 1th arrival becomes invalid, OPAM corrects the remaining arrivals of j . Let o and d be, respectively, the original and mutated k th arrival time of j . For all the arrivals of j after d , OPAM first updates their original arrival time values by adding the difference d − o . Let \(\mathbb {T} = [0,\mathbf {T}]\) be the scheduling period. OPAM then removes some arrivals of j if they are mutated to arrive after T or adds new arrivals of j while ensuring that all tasks arrive within \(\mathbb {T}\) .

As shown in lines 20–26 in Figure  5 , the internal fitness of the generated offspring is computed based on the P population. OPAM then updates the A population of task-arrival sequences by comparing the offspring and individuals in A (line 27).

We note that when a system is only composed of periodic tasks, OPAM will skip evolving for worst-case arrival sequences as arrivals of periodic tasks are deterministic (see Section  3 ). Nevertheless, OPAM will optimize priority assignments based on given arrivals of periodic tasks. When needed, OPAM can be easily extended to manipulate offset and period values for periodic tasks, in a way identical to how we currently handle inter-arrival times for aperiodic tasks.

6.5 Evolution: Best-case priority assignments

Figure  6 shows the evolution procedure of priority assignments, which refines lines 23–26 in Figure  4 . OPAM tailors the Non-dominated Sorting Genetic Algorithm version 2 (NSGAII) (Deb et al. 2002 ) to generate a non-dominating (equally viable) set of priority assignments, representing the best trade-offs found among the given internal fitness functions. This is referred to as a Pareto nondominated front (Knowles and Corne 2000 ), where the dominance relation over priority assignments is defined as follows: A priority assignment \(\rightarrow {P}\) dominates another priority assignment \(\rightarrow {P}^{\prime }\) if \(\rightarrow {P}\) is not worse than \(\rightarrow {P}^{\prime }\) in all fitness values, and \(\rightarrow {P}\) is strictly better than \(\rightarrow {P}^{\prime }\) in at least one fitness value. NSGAII has been applied to many multi-objective optimization problems (Langdon et al. 2010 ; Shin et al. 2018 ; Wang et al. 2020 ).

An NSGAII-based algorithm for evolving priority assignments

OPAM maintains a population P of priority assignments as an archive that contains the best priority assignments discovered during coevolution. Unlike a standard application of NSGAII, in our study, we need to reevaluate the internal fitness values for priority assignments in P at every generation as the internal fitness values are computed based on the A population of task-arrival sequences, which coevolves. As shown in lines 9–16 in Figure  6 , OPAM first computes the internal fitness functions that measure the magnitude of safety margins and the extent of constraint satisfaction. OPAM then sorts non-dominated Pareto fronts (line 19) and assigns crowding distance (line 20) to introduce diversity among non-dominated priority assignments (Deb et al. 2002 ).

For breeding the next population of priority assignments (line 21 in Figure  6 , OPAM applies the following standard genetic operators (Sivanandam and Deepa 2008 ) that have been applied to many similar problems (Islam et al. 2012 ; Marchetto et al. 2016 ; Shin et al. 2018 ): (1) Selection. OPAM uses a binary tournament selection based on non-domination ranking and crowding distance. The binary tournament selection has been used in the original implementation of NSGAII (Deb et al. 2002 ). (2) Crossover. OPAM applies a partially mapped crossover (PMX) (Goldberg and Lingle 1985 ). PMX ensures that the generated offspring are valid permutations of priorities. (3) Mutation. OPAM uses a permutation swap method for mutating a priority assignment. This mutation method interchanges two randomly-selected priorities in a priority assignment according to a given mutation probability.

For the generated population P α of priority assignments, OPAM computes the two internal fitness functions (lines 22–29 in Figure  6 ). OPAM then sorts non-dominated Pareto fronts for the union of the current P and next P α populations (line 30), assign crowding distance (line 31), and select the best archive by accounting for the computed non-domination ranking and crowding distance (line 32).

6.6 External fitness evaluation

Figure  7 shows an algorithm that computes the external fitness functions and finds the best Pareto front, which refines lines 28–31 in Figure  4 . To monitor the coevolution progress in a stable manner, OPAM takes as input a set E of task-arrival sequences that are generated independently from the coevolution process. We use an adaptive random search technique (Chen et al. 2010 ) to sample task-arrival sequences in order to create E . The adaptive random search extends the naive random search by maximizing the Euclidean distance between the sampled points such that it maximizes the diversity of task-arrival sequences in E .

An algorithm for evaluating external fitness and finding the best Pareto front

As shown in lines 9–16 in Figure  7 , OPAM computes the two external fitness values for each priority assignment in the P population based on a given set E of task-arrival sequences. OPAM then sorts non-dominated Pareto fronts for the union of the P population and the current best Pareto front (line 17), assigns crowding distance (line 18), and selects the best Pareto front by accounting for the computed non-domination ranking and crowding distance (line 32). OPAM adopts NSGAII in order to maximize the diversity of priority assignments in the best Pareto front.

7 Evaluation

This section describes our evaluation of OPAM through six industrial case studies from different domains and several synthetic subjects. Our full evaluation package is available online (Lee et al. 2021 ).

7.1 Research questions

How does OPAM perform compared with Random Search? For search-based solutions, this RQ is an important sanity check to ensure that success is not due to the search problem being easy (Arcuri and Briand 2014 ). Our conjecture is that a search-based algorithm, although expensive, will significantly outperform naive random search (RS).

Is competitive coevolution suitable to find best-case priority assignments? We conjecture that a coevolutionary algorithm is a suitable solution to address the priority assignment problem since it is solved, in practice, through a competing interactive process between the development and testing teams. To answer this RQ, we compare OPAM with a sequential approach that first looks for worst-case sequences of task arrivals and then tries to find best-case priority assignments.

Can OPAM find (near-)optimal solutions for large-scale systems in a reasonable time budget? In this RQ, we investigate the scalability of OPAM by conducting some experiments with systems of various sizes, including six industrial and several synthetic subjects. We study the relationship between OPAM’s performance measures and the characteristics of study subjects.

How do priority assignments generated by OPAM compare with priority assignments defined by engineers? OPAM can be considered useful only when it finds priority assignments that show benefits over those defined (manually) by engineers with domain expertise. This RQ therefore compares the quality of priority assignments generated by OPAM with those defined by engineers. We further discuss the usefulness of OPAM from a practical perspective, based on the feedback received from engineers in LuxSpace.

7.2 Industrial study subjects

To evaluate RQs in realistic and diverse settings, we apply OPAM to six industrial study subjects from different domains such as aerospace, automotive, and avionics domains. Specifically, we obtained one case study subject from our industry partner, LuxSpace. We found the other five industrial study subjects in the literature (Di Alesio et al. 2015 ), which, consistent with the LuxSpace system, all assume a single-queue, multi-core, fixed-priority scheduling policy. Note that OPAM uses the same scheduling policy (described in Section  6.2 ) as in Di Alesio et al.’s work. This policy uses fixed priorities that are determined offline and therefore do not change dynamically. Table  2 summarizes the relevant attributes of these subjects, presenting the number of periodic and aperiodic tasks, resource dependencies, triggering relations, and platform cores. The subjects are characterized by real-time parameters, e.g., periods, deadlines, and priorities, described in Section  3 . We note that all the study subjects are deadlock-free systems as they do not have circular resource dependencies. Regarding task priorities, all tasks in the six subjects have fixed priorities, which are defined by experts in their domains. The full task descriptions (including WCET, inter-arrival times, periods, deadlines, priorities, and relationship details) of the subjects are available online (Lee et al. 2021 ). The main missions of the six subjects are described as follows:

ICS is an ignition control system that checks the status of an automotive engine and corrects any errors of the engine (Peraldi-Frati and Sorel 2008 ). The system was developed by Bosch GmbH. Footnote 1

CCS is a cruise control system that acquires data from vehicle sensors and maintains the specified vehicle speed (Anssi et al. 2011 ). Continental AG Footnote 2 developed the system.

UAV is a mini unmanned air vehicle that follows dynamically defined way-points and communicates with a ground station to receive instructions (Traore et al. 2006 ). The system was developed in collaboration with the University of Poitiers France and ENSMA. Footnote 3

GAP is a generic avionics platform for a military aircraft (Locke et al. 1990 ). The system was designed in a joint project with Carnegie Mellon University, the US Navy, and IBM Footnote 4 , aiming at supporting several missions regarding air-to-surface attacks.

HPSS is a satellite system for two satellites, named Herschel and Planck (Mikučionis et al. 2010 ). The two satellites share the same computational architecture, although they have different scientific missions. Herschel aims at studying the origin and evolution of stars and galaxies. Planck’s primary mission is the study of the relic radiation from the Big Bang. ESA Footnote 5 carried out the HPSS project.

ESAIL is a microsatellite for tracking ships worldwide by detecting messages that ships radio-broadcast (see Section  2 ). Luxspace, our industry partner, developed ESAIL in an ESA project.

7.3 Synthetic study subjects

To investigate RQ3, we use synthetic subjects in order to freely control key parameters in real-time systems. We create a set of tasks by adopting a well-known procedure (Emberson et al. 2010 ) for synthesizing real-time tasks, which has been applied in many schedulability analysis studies (Davis et al. 2008 ; Zhang and Burns 2009 ; Davis and Burns 2011 ; Grass and Nguyen 2018 ; Du̇rr et al. 2019 ).

Figure  8 describes a procedure that synthesizes a set of real-time tasks. For a given number n of tasks and a target utilization u t , the procedure first generates a set U of task utilization values by using the UUniFast-Discard algorithm (Davis and Burns 2011 ) (line 13). The UUniFast-Discard algorithm is devised to give an unbiased distribution of utilization values, where a utilization U j ∈ U is a positive value and \({\sum }_{U_{j} \in \mathbf {U}} U_{j} = u_{t}\) .

An algorithm for synthesizing a set of tasks

The procedure then generates a set I of n task periods according to a log-uniform distribution within a range [ p d m i n , p d m a x ], i.e., given a task period (random variable) I j ∈ I , \(\log {I_{j}}\) follows a uniform distribution (line 14 in Figure  8 ). For example, when the minimum and maximum task periods are p d m i n = 10ms and p d m a x = 1000ms, respectively, the procedure generates (approximately) an equal number of tasks in time intervals [10ms, 100ms] and [100ms, 1000ms]. The parameter g is used to choose the granularity of the periods, i.e., task periods are multiples of g . Such a distribution of task periods provides a reasonable degree of realism with respect to what is usually observed in real systems (Baruah et al. 2011 ).

As shown in lines 15–16 of the procedure in Figure  8 , a set C of task WCETs are computed based on the set U of task utilization values and the set I of task periods. Specifically, a task WCET C j ∈ C is computed as C j = U j ⋅ I j .

As per line 17 of the listing in Figure  8 , the procedure synthesizes a set S of tasks. A task j is characterized by a period I j and a WCET C j and it is associated with a deadline d l ( j ) and a priority p r ( j ). According to the rate-monotonic scheduling policy (Liu and Layland 1973 ), tasks’ deadlines are equal to their periods and tasks with shorter periods are given higher priorities.

To synthesize aperiodic tasks, the procedure converts some periodic tasks to aperiodic tasks according to a given ratio γ of aperiodic tasks among all tasks (see line 19 in Figure  8 ). A range factor μ is used to determine maximum inter-arrival times of aperiodic tasks. Specifically, for a task j to be converted, the procedure sets the minimum inter-arrival time p m i n ( j ) as p m i n ( j ) = I j . The procedure then selects a uniformly distributed value x from the range (1, μ ] and computes the maximum inter-arrival time p m a x ( j ) as p m a x ( j ) = x ⋅ I j .

7.4 Experimental Design

This section describes how we design experiments to answer the RQs described in Section  7.1 . We conducted four experiments, EXP1, EXP2, EXP3, and EXP4, as described below.

To answer RQ1, EXP1 compares OPAM with our baseline, which relies on random search, to ensure that the effectiveness of OPAM is not due to the search problem being simple. Our baseline, named RS, replaces GA with a random search for finding worst-case sequences of task arrivals and NSGAII with a random search for finding best-case priority assignments. Note that RS uses the same internal and external fitness functions (see Section  6.3 ) and also maintains the best populations during search; however, it does not employ any genetic operators, i.e., crossover and mutation. In EXP1, we applied OPAM and RS to the six industrial subjects described in Section  7.2 .

Recall from Section  6.3 that OPAM uses a set E of task-arrival sequences that are generated independently from the coevolution process in order to monitor the coevolution progress in a stable manner. As OPAM and RS use the same set E of task-arrival sequences, EXP1 first compares OPAM and RS based on E . In addition, EXP1 examines how well the solutions, i.e., priority assignments, found by OPAM and RS perform with other sequences of task arrivals. To do so, we create six sets of sequences of task arrivals for each study subject by varying the method to generate task-arrival sequences and the number of task-arrival sequences. Note that task-arrival sequences generated by different methods are valid with respect to the inter-arrival times defined in each study subject. Below we describe the six sets of task-arrival sequences generated for each subject.

\(\mathbf {T}_{a}^{10}\) : A set of task-arrival sequences generated by using an adaptive random search technique (Chen et al. 2010 ) that aims at maximizing the diversity of task-arrival sequences. The \(\mathbf {T}_{a}^{10}\) set contains 10 sequences of task arrivals.

\(\mathbf {T}_{w}^{10}\) : A set of task-arrival sequences generated by using a stress test case generation method that aims at maximizing the chances of deadline misses in task executions. The stress test case generation method extends prior work (Briand et al. 2005 ). The extended method uses the fitness function regarding deadline misses and genetic operators that OPAM introduces for evolving worst-case task-arrival sequences (see Section  6 ). The \(\mathbf {T}_{w}^{10}\) set contains 10 sequences of task arrivals.

\(\mathbf {T}_{r}^{10}\) : A set of task-arrival sequences generated randomly. The \(\mathbf {T}_{r}^{10}\) set has 10 sequences of task arrivals.

\(\mathbf {T}_{a}^{500}\) : A set of task-arrival sequences generated by using the adaptive random search technique. The \(\mathbf {T}_{a}^{500}\) set contains 500 sequences of task arrivals.

\(\mathbf {T}_{w}^{500}\) : A set of task-arrival sequences generated by using the stress test case generation method. The \(\mathbf {T}_{w}^{500}\) set contains 500 sequences of task arrivals.

\(\mathbf {T}_{r}^{500}\) : A set of task-arrival sequences generated randomly. The \(\mathbf {T}_{r}^{500}\) set has 500 sequences of task arrivals.

To answer RQ2, EXP2 compares OPAM with a priority assignment method, named SEQ, that relies on one-population search algorithms. SEQ first finds a set of worst-case sequences of task arrivals using GA with the fitness function that measures the magnitude of deadline misses (see f d () in Section  6.3 ) and the genetic operators described in Section  6.4 . Given a set of worst-case task-arrival sequences obtained from GA, SEQ then aims at finding best-case priority assignments using NSGAII with the fitness functions that quantify the magnitude of safety margins and the degree of constraint satisfaction (see f s () and f c (), respectively, in Section  6.3 ) and the genetic operators described in Section  6.5 .

We note that SEQ does not use the external fitness functions as it does not coevolve task-arrival sequences and priority assignments. Hence, the numbers of fitness evaluations of the two methods are not comparable. To fairly compare OPAM and SEQ, we set the same time budget for the two methods. Specifically, we first measure the execution time of OPAM for analyzing each subject. We then split the execution time in half and set each half time as the execution budget of the GA and NSGAII steps in SEQ for the corresponding subject. In order to assess the quality of priority assignments obtained from OPAM and SEQ, we use the sets of task-arrival sequences described in EXP1, i.e., \(\mathbf {T}_{a}^{10}\) , \(\mathbf {T}_{w}^{10}\) , \(\mathbf {T}_{r}^{10}\) , \(\mathbf {T}_{a}^{500}\) , \(\mathbf {T}_{w}^{500}\) , and \(\mathbf {T}_{r}^{500}\) , which are created independently from the two methods.

To answer RQ3, EXP3 examines not only the six industrial subjects but also 370 synthetic subjects. We create the synthetic subjects to study correlations between the execution time and memory usage of OPAM and the following parameters: the number of tasks ( n ), a (part-to-whole) ratio of aperiodic tasks ( γ ), a range factor for maximum inter-arrival times ( μ ), and simulation time ( T ), as described in Sections  7.3 and  6 . We note that we chose to control parameters n , γ , and μ because they are the main parameters on which engineers have control to define tasks in real-time systems. Simulation time T obviously impacts the execution time of OPAM as well. But EXP3 aims at modeling such correlations precisely and providing experimental results. Regarding the other factors that define, for example, task relationships and platform cores, we note significant diversity across the six industrial subjects.

Recall from Section  7.3 that we use the task generation procedure presented in Figure  8 to synthesize tasks. For EXP3, we set some parameter values of the procedure as follows: (1) Target utilization u t = 0.7, which is a common objective in the development of a real-time system in order to guarantee the schedulability of tasks (Fineberg and Serlin 1967 ; Du̇rr et al. 2019 ). (2) The range of task periods [ p d m i n , p d m a x ] = [10ms,1s], which are common values in many real-time systems (Emberson et al. 2010 ; Baruah et al. 2011 ). (3) The granularity of task periods g = 10ms in order to increase realism as most of the task periods in our industrial subjects are multiples of 10ms. Because of some degree of randomness in the procedure of Figure  8 , we create ten synthetic subjects per configuration. Below we further describe how synthetic subjects are created for each controlled experiment. EXP3.1. To study the correlations between the execution time and memory usage of OPAM with the number of tasks n , we create nine sets of ten synthetic subjects such that no two sets have the same number of tasks. Specifically, we create sets with 10, 15, ..., 50 tasks, respectively. Regarding the ratio of aperiodic tasks, γ = 0.4 as, on average, the ratio of aperiodic tasks to periodic tasks in our industrial subjects is 2/3. For the range factor, μ = 2, which is determined based on the inter-arrival times of aperiodic tasks in our industry subjects. We set the simulation time T to 2s in order to ensure that any aperiodic task arrives at least once during that time. We note that, given the maximum task period p d m a x = 1s and the range factor μ = 2, the maximum inter-arrival time of an aperiodic task is at most 2s (see Section  7.3 ). EXP3.2. To study the correlations between the execution time and memory usage of OPAM with the ratio of aperiodic tasks γ , we create ten sets of synthetic subjects by setting this ratio to the following values: 0.05, 0.10, ..., 0.50. We set the number of tasks to 20 ( n = 20), which is the average number of tasks in our six industrial subjects. Regarding the other parameters, range factor and simulation time, μ = 2 and T = 2s are set as discussed in EXP3.1. EXP3.3. To study the correlations between the execution time and memory usage of OPAM with the range factor μ that is used to determine the maximum inter-arrival times, we create nine sets of synthetic subjects by setting μ to 2, 3, ..., 10. We set the simulation time as follows: T = 10s. This ensures that any aperiodic task arrives at least once during the simulation time when μ is at most 10 (see Section  7.3 ). The other parameters, the number of tasks and ratio of aperiodic tasks, n = 20 and γ = 0.4 are set as discussed in EXP3.1 and EXP3.2. EXP3.4. To study the correlations between the execution time and memory usage of OPAM with the simulation time T , we create nine sets of synthetic subjects by setting T to 2s, 3s, ..., 10s. The other parameters, e.g., the number of tasks, the ratio of aperiodic tasks, and the range factor, n = 20, γ = 0.4, and μ = 2, are set as discussed in EXP3.1 and EXP3.2.

To answer RQ4, EXP4 compares priority assignments optimized by OPAM and those defined by engineers. We apply OPAM to the six industrial subjects (see Section  7.2 ) which include priority assignments defined by practitioners. Note that we focus here on the ESAIL subject in collaboration with our industry partner, LuxSpace; The other five subjects are from the literature (Di Alesio et al. 2015 ) and hence we can only collect feedback from practitioners for ESAIL.

7.5 Evaluation metrics

Multi-objective evaluation metrics..

In order to fairly compare the results of search algorithms, based on existing guidelines (Li et al. 2020 ) for assessing multi-objective search algorithms, we use complementary quality indicators: Hypervolume (HV) (Zitzler and Thiele 1999 ), Pareto Compliant Generational Distance (GD+) (Ishibuchi et al. 2015 ), and Spread (Δ) (Deb et al. 2002 ). To compute the GD+ and Δ quality indicators, following the usual procedure (Li et al. 2020 ), we create a reference Pareto front as the union of all the non-dominated solutions obtained from all runs of the algorithms being compared. Identifying the optimal (ideal) Pareto front is typically infeasible for a complex optimization problem (Li et al. 2020 ). Key features of the three quality indicators are described below.

HV is defined to measure the volume in the objective space that is covered by members of a Pareto front generated by a search algorithm (Zitzler and Thiele 1999 ). The higher the HV values, the more optimal the search outputs.

GD+ is defined to measure the distance between the points on a Pareto front obtained from a search algorithm and the nearest points on a reference Pareto front (Ishibuchi et al. 2015 ). GD+ modifies General Distance (GD) (Veldhuizen and Lamont 1998 ) to account for the dominance relations when computing the distances. The lower the GD+ values, the more optimal the search outputs.

Δ is defined to measure the extent of spread among the points on a Pareto front computed by a search algorithm (Deb et al. 2002 ). We note that OPAM aims at obtaining a wide variety of equally-viable priority assignments on a Pareto front (see Section  6 ). The lower the Spread values, the more spread out the search outputs.

Interpretable metrics.

The two external fitness functions described in Section  6 mainly aim at effectively guiding search. It is, however, difficult for practitioners to interpret the computed fitness values. Since they are not intuitive to practitioners, to assess the usefulness of OPAM from a practitioner perspective, we measure (1) the safety margins from tasks’ completion times to their deadlines across our experiments and (2) the number of constraint violations in a priority assignment. In addition, we measure the execution time and memory usage of OPAM.

Statistical comparison metrics.

To statistically compare our experiment results, we use the Mann-Whitney U-test (Mann and Whitney 1947 ) and Vargha and Delaney’s \(\hat {A}_{12}\) effect size (Vargha and Delaney 2000 ), which have been frequently applied for evaluating search-based algorithms (Arcuri et al. 2010 ; Hemmati et al. 2013 ; Shin et al. 2018 ). Mann-Whitney U-test determines whether two independent samples are likely or not to belong to the same distribution. We set the level of significance, α , to 0.05. Vargha and Delaney’s \(\hat {A}_{12}\) measures probabilistic superiority – effect size – between search algorithms. Two algorithms are considered to be equivalent when the value of \(\hat {A}_{12}\) is 0.5.

7.6 Parameter tuning and implementation

Parameters for coevolutionary search..

For the coevolutionary search parameters, we set the population size to 10, the crossover rate to 0.8, and the mutation rate to 1/| J |, where | J | denotes the number of tasks. We apply these parameter values for both the evolution of task-arrival sequences and priority assignments (see Section  6 ). These values are determined based on existing guidelines (Arcuri and Fraser 2011 ; Sayyad et al. 2013 ) and previous work (Lee et al. 2020b ).

We determine the number of coevolution cycles (see Section  6 ) based on an initial experiment. We applied OPAM to the six industrial subjects and ran OPAM 50 times for each subject. From the experiment results, we observed that there is no notable difference in Pareto fronts generated after 1000 cycles. Hence, we set the number of coevolution cycles to 1000 in our experiments, i.e., EXP1, EXP2, and EXP3 described in Section  7.4 .

Parameters for evaluating fitness functions.

To evaluate external fitness functions, we use a set of task-arrival sequences that are generated independently from the coevolution process (see Section  6.6 ). We use an adaptive random search (Chen et al. 2010 ) to generate a set E of task-arrival sequences, which varies task arrival times within the specified inter-arrival time ranges of aperiodic tasks. We set the size of E to 10. From our initial experiment, we observed that this is sufficient to compute the external fitness functions of OPAM under a reasonable time, i.e., less than 15s. We note that E contains two default sequences of task arrivals as follows: (seq. 1) aperiodic tasks always arrive at their maximum inter-arrival times and (seq. 2) aperiodic tasks always arrive at their minimum inter-arrival times. By having those two sequences of task arrivals as initial elements in E , the adaptive random search finds other sequences of task arrivals to maximize the diversity of elements in E .

If a system contains only periodic tasks, the simulation time is often set as the least common multiple (LCM) of their periods to account for all possible arrivals (Peng et al. 1997 ). However, as the six industrial subjects include aperiodic tasks, this is not applicable. For the experiments with the six industrial subjects, we set the simulation time to the maximum time between the LCM of periodic tasks’ periods and the maximum inter-arrival time among aperiodic tasks. By doing so, all possible arrival patterns of periodic tasks are examined and any aperiodic task arrives at least once during simulation. Recall from Section  6.4 that OPAM varies arrival times of aperiodic tasks to find worst-case sequences of task arrivals.

We note that the parameters mentioned above can probably be further tuned to improve the performance of our approach. However, since with our current setting, we were able to convincingly and clearly support our conclusions, we do not report further experiments on tuning those values.

Implementation.

We implemented OPAM by extending jMetal (Durillo and Nebro 2011 ), which is a metaheuristic optimization framework supporting NSGAII and GA. We conducted our experiments using the high-performance computing cluster (Varrette et al. 2014 ) at the University of Luxembourg. To account for randomness, we repeated each run of OPAM 50 times for all experiments. Each run of OPAM was executed on a different node (equipped with five 2.5GHz cores and 20GB memory) of the cluster, and took less than 16 hours.

7.7 Results

Figure  9 shows the best Pareto fronts obtained with 50 runs of OPAM and RS, for the six industrial study subjects described in Section  7.2 . The fitness values presented in the figures are computed based on each subject’s set E of task-arrival sequences (see Section  7.6 ), which is created independently from OPAM and RS. Figures  9a , c, d, e, and d indicate that OPAM finds significantly better solutions than RS for ICS, UAV, GAP, HPSS, and ESAIL. Regarding CCS (see Figure  9b ), it is difficult to conclude anything based only on visual inspection. Hence, we compared Pareto fronts obtained by OPAM and RS using the three quality indicators HV, GD+, and Δ, described in Section  7.5 .

Pareto fronts obtained by OPAM and RS for the six industrial subjects: (a) ICS, (b) CCS, (c) UAV, (d) GAP, (e) HPSS, and (f) ESAIL. The fitness values are computed based on each subject’s set E of task-arrival sequences (see Section  7.6 ). The points located closer to the bottom left of each plot are considered to be better priority assignments when compared to points closer to the top right

Figure  10 depicts distributions of HV (Figure  10a ), GD+ (Figure  10b ), and Δ (Figure  10c ) for the six industrial subjects. The boxplots in the figures present the distributions (25%-50%-75%) of the quality values obtained from 50 runs of OPAM and RS. The quality values are computed based on the Pareto fronts obtained by the algorithms and each subject’s set E of task-arrival sequences (see Section  7.6 ). In the figures, statistical comparisons of the two corresponding distributions are summarized using p-values and \(\hat {A}_{12}\) values, as described in Section  7.5 , under each subject name.

Comparing OPAM and RS using the three quality indicators: (a) HV, (b) GD+, and (c) Δ. The boxplots (25%-50%-75%) show the quality values obtained from 50 runs of OPAM and RS. The quality values are computed based on the Pareto fronts obtained by the algorithms and each subject’s set E of task-arrival sequences (see Section  7.6 )

As shown in Fig.  10a and b, OPAM obtains better distributions of HV and GD+ compared to RS for all six subjects. All the differences are statistically significant as the p-values are below 0.05. Regarding Δ, as depicted in Fig.  10c , OPAM yields higher diversity in Pareto front solutions than RS for the following subjects: UAV, GAP, and HPSS. For ICS, CCS, and ESAIL, OPAM and RS obtain similar Δ values. From Fig.  10a and b, and Table  2 , we also observe that the higher the number of aperiodic tasks in a subject, the larger the differences in HV and GD+ between OPAM and RS. Hence, for these two quality indicators, OPAM outperforms RS more significantly for more complex search problems. Note that the number of aperiodic tasks is one of the main factors that drives the degree of uncertainty in task arrivals.

Given the Pareto priority assignments obtained by OPAM and RS, we further assessed the quality values of the solutions by evaluating them with different sets of task-arrival sequences. As described in Section  7.4 , we created six test sets of task-arrival sequences for each subject by varying the sequence generation methods and the number of task-arrival sequences in a set (see \(\mathbf {T}_{a}^{10}\) , \(\mathbf {T}_{w}^{10}\) , \(\mathbf {T}_{r}^{10}\) , \(\mathbf {T}_{a}^{500}\) , \(\mathbf {T}_{w}^{500}\) , and \(\mathbf {T}_{r}^{500}\) described in Section  7.4 ). Table  3 reports the average quality values measured by HV, GD+, and Δ based on 50 runs of OPAM and RS with the different test sets of task-arrival sequences. The results indicate that OPAM significantly outperforms RS in most comparison cases. Specifically, out of a total of 108 comparisons, OPAM outperforms RS 87 times (see the blue-colored cells related to OPAM in Table  3 ). Regarding Δ, RS outperforms OPAM for the CCS subject (see the gray-colored cells related to RS in Table  3 ). As shown in Table  2 , CCS has only 3 aperiodic tasks and RS was therefore able to find better solutions with respect to Δ for such a simple subject.

To compare OPAM and SEQ, we first visually inspect the best Pareto fronts obtained from 50 runs of OPAM and SEQ for the six study systems described in Section  7.2 by varying the test sets of task-arrival sequences for each subject (see \(\mathbf {T}_{a}^{10}\) , \(\mathbf {T}_{w}^{10}\) , \(\mathbf {T}_{r}^{10}\) , \(\mathbf {T}_{a}^{500}\) , \(\mathbf {T}_{w}^{500}\) , and \(\mathbf {T}_{r}^{500}\) described in Section  7.4 ), which are created independently from OPAM and SEQ. Overall, we observed that OPAM finds significantly better priority assignments in most cases. For example, Figure  11 depicts the best Pareto fronts obtained by OPAM and SEQ when the fitness values are computed based on each subject’s test set \(\mathbf {T}_{a}^{500}\) of 500 task-arrival sequences, which are generated with adaptive random search. The results clearly show that OPAM outperforms SEQ with respect to producing more optimal Pareto fronts for ICS, CCS, UAV, HPSS, and ESAIL. For GAP, the visual inspection is not sufficient to provide any conclusions. Hence, we further compare OPAM and SEQ based on the quality indicators described in Section  7.5 .

Pareto fronts obtained by OPAM and SEQ for the six industrial subjects: (a) ICS, (b) CCS, (c) UAV, (d) GAP, (e) HPSS, and (f) ESAIL. The fitness values are computed based on each subject’s set \(\mathbf {T}_{a}^{500}\) of task-arrival sequences (see Section  7.4 ). The points located closer to the bottom left of each plot are considered to be better priority assignments when compared to points closer to the top right

Table  4 compares the quality values measured by HV, GD+, and Δ for the six study subjects. To fairly compare the priority assignments obtained by OPAM and SEQ, we assess them with the test sets of task-arrival sequences for each subject (see \(\mathbf {T}_{a}^{10}\) , \(\mathbf {T}_{w}^{10}\) , \(\mathbf {T}_{r}^{10}\) , \(\mathbf {T}_{a}^{500}\) , \(\mathbf {T}_{w}^{500}\) , and \(\mathbf {T}_{r}^{500}\) described in Section  7.4 ). Table  4 reports the average quality values computed based on 50 runs of OPAM and SEQ. In Table  4 , the statistical comparison of the two corresponding distributions are reported using p-values and \(\hat {A}_{12}\) values.

As shown in Table  4 , we compared OPAM and SEQ 108 times by varying the study subjects, the quality indicators, the number of task-arrival sequences, and the task-arrival sequence generation methods. Out of 108 comparisons, OPAM significantly outperforms SEQ 63 times. Specifically, out of 36 HV comparisons, OPAM obtains better HV values than SEQ 28 times. For ICS (6 HV comparisons), the differences in HV values between OPAM and SEQ are not statistically significant. In only one HV comparison for CCS, SEQ outperforms OPAM (see the gray-colored cell related to HV and CCS in Table  4 ). To interpret these results, one must recall from Table  2 that ICS and CCS have only three aperiodic tasks that impact the degree of uncertainty in task arrivals and therefore represent simple cases. Out of 36 GD+ comparisons, OPAM outperforms SEQ 32 times. SEQ outperforms OPAM only two times for CCS. Hence, overall, the results indicate that OPAM outperforms SEQ, in terms of generating more optimal Pareto fronts, when the subjects feature a considerable degree of uncertainty in task arrivals and therefore make our search problem more complex. Otherwise differences are not statistically or practically significant. Regarding Δ, which focuses on the diversity of solutions on the Pareto front, SEQ outperforms OPAM 24 times out of 36 comparisons (see the gray-colored cells related to Δ in Table  4 ). However, since OPAM produces enough alternative priority assignments spreading across Pareto fronts (as visible from the solutions obtained by OPAM in Figure  11 ), these differences in Δ have limited implications in practice.

Table  5 reports the average execution times and memory usage required to run OPAM for the six industrial subjects, over 50 runs. As shown in Table  5 , finding optimal priority assignments for ESAIL requires the largest execution time (≈ 15.5h) and memory usage (≈ 2.9GB), compared to the other subjects. We note that such execution time and memory usage are acceptable as OPAM can be executed offline in practice.

Figures  12 and  13 show, respectively, the execution times and memory usage from EXP3.1 (a), EXP3.2 (b), EXP3.3 (c), and EXP3.4 (d), described in Section  7.4 . The boxplots in the figures show distributions (25%-50%-75%) obtained from 50 × 10 runs of OPAM for a set of 10 synthetic subjects, which are created with the same experimental setting. Regarding the execution time of OPAM, Figures  12a and d show that the execution time of OPAM is linear both in the number of tasks and simulation time. As for the memory usage of OPAM, results in Figures  13a and d indicate that memory usage is linear both in the number of tasks and in the simulation time. However, the results depicted in Figures  12b , c,  13b , and c indicate that there are no correlations between OPAM execution time and memory usage and the following two parameters: ratio of aperiodic tasks and range factor. Therefore, we expect OPAM to scale well as the numbers of tasks and simulation time increase.

Execution times of OPAM when varying the values of the following parameters: (a) number of tasks n , (b) ratio of aperiodic tasks γ , (c) range factor μ , and (d) simulation time T . The boxplots (25%-50%-75%) show the execution times obtained from 500 runs of OPAM, i.e., 50 runs for each of the 10 synthetic subjects with the same configuration

Memory usage of OPAM when varying the values of the following parameters: (a) number of tasks n , (b) ratio of aperiodic tasks γ , (c) range factor μ , and (d) simulation time T . The boxplots (25%-50%-75%) show the memory usage obtained from 500 runs of OPAM, i.e., 50 runs for each of the synthetic subjects with the same configuration

Figure  14 compares, with respect to external fitness (see the f s () and f c () fitness functions and the set E of sequences of task arrivals described in Section  6.6 ), the Pareto solutions obtained by OPAM against the priority assignments defined by engineers for the six industrial subjects: ICS (Figure  14a ), CCS (Figure  14b ), UAV (Figure  14c ), GAP (Figure  14d ), HPSS (Figure  14e ), and ESAIL (Figure  14f ).

Comparing Pareto solutions obtained by OPAM and priority assignments defined by engineers for the six industrial subjects: (a) ICS, (b) CCS, (c) UAV, (d) GAP, (e) HPSS, and (f) ESAIL. The points located closer to the bottom left of each plot are considered to be better priority assignments when compared to points closer to the top right

As shown in the figure, the solutions obtained by OPAM clearly outperform the priority assignments defined by engineers regarding the two external objectives: the magnitude of safety margins and the extent to which constraints are satisfied.

Table  6 summarizes safety margins from the task executions of ESAIL when using one of our priority assignments optimized by OPAM and the one defined by engineers at LuxSpace. Note that we focus on ESAIL as it is not possible to access the engineers who developed the other five industrial subjects reported in the literature (Locke et al. 1990 ; Traore et al. 2006 ; Peraldi-Frati and Sorel 2008 ; Mikučionis et al. 2010 ; Anssi et al. 2011 ). For comparison, we chose the bottom-left solution in Fig.  14f since it is optimal for the constraint fitness, which is the same as the fitness value of the priority assignment defined by engineers, and the differences in safety margin fitness among our solutions are negligible.

As shown in Table  6 , our optimized priority assignment significantly outperforms the one of engineers. Our solution increases safety margins, on average, by 5.33% compared to the engineers’ solution. For aperiodic tasks, our solution decreases safety margins by 0.01% (4.2ms difference) when the safety margins being compared are the maximum margins observed in both solutions (see the maximum safety margins, 59710.3ms obtained by engineers’ solution and 59707.2ms obtained by OPAM, in Table  6 ). Such a small decrease is however negligible in the context of ESAIL as the maximum safety margin obtained by our solution is still large, i.e., ≈ 1m. For periodic tasks, we note that our solution increases safety margins by 208.09% when the safety margins being compared are the minimum margins observed in both solutions (see the minimum safety margins, -44.5ms obtained by engineers’ solution and 48.1ms obtained by OPAM, in Table  6 ). Note that the minimum safety margin of -44.5ms obtained with the engineers’ solution indicates that a task violates its deadline. In the context of ESAIL, which is a mission-critical system, such gain in safety margins in the executions of periodic tasks is important because the hard deadlines of periodic tasks are more critical than the soft deadlines of aperiodic tasks.

Investigating practitioners’ perceptions of the benefits of OPAM is necessary to adopt OPAM in practice. To do so, we draw on the qualitative reflections of three software engineers at LuxSpace, with whom we have been collaborating on this research. They have had four to seven years of experience developing satellite systems at LuxSpace, with more than 50 years of collective experience in companies. All the reflections are based on observations made throughout our interactions. The engineers at LuxSpace deemed OPAM to be an improvement over their current practice as it allows them to perform domain-specific trade-off analysis among Pareto solutions and is useful in practice to support decision making with respect to their task design. Encouraged by the promising results, we are now applying OPAM to new systems in collaboration with LuxSpace.

7.8 Threats to Validity

To mitigate the main threats that arise from not accounting for random variation, we compared OPAM against RS under identical parameter settings. We present all the underlying parameters and provide the full package of our experiments to facilitate replication. Also, we ran OPAM 50 times for each study subject and compared results using statistical analysis, i.e., Mann-Whitney U-test and Vargha and Delaney’s \(\hat {A}_{12}\) .

We note that there are prior studies that aim at optimizing priority assignments such as OPA (Audsley 1991 ) and RPA (Davis and Burns 2007 ). However, to our knowledge, none of the existing works offer ways to analyze trade-offs among equally viable priority assignments with respect to safety margins and the satisfaction of constraints. Nevertheless, we attempted to compare OPAM with an extension of an existing method, e.g., RPA (Davis and Burns 2007 ). To do so, we first applied an exhaustive schedulability analysis technique to the ESAIL subject – our motivating case study – in order to verify whether the ESAIL tasks are schedulable for a given priority assignment. Note that existing priority assignment techniques are built on such schedulability analysis methods, which are therefore a prerequisite. We chose UPPAAL (Behrmann et al. 2004 ), a model checker, for schedulability analysis as it has been used in real-time system studies (Mikučionis et al. 2010 ; Yu et al. 2010 ; Yalcinkaya et al. 2019 ). However, our experiment results using UPPAAL for ESAIL showed that it was not able to complete the analysis task, even after 5 days of execution, for a single priority assignment. We were therefore not able to perform experimental comparisons with existing priority assignment methods. Since this evaluation is not the main focus of this article, we point the reader to the UPPAAL specification of ESAIL available online (Lee et al. 2021 ).

Recall from Section  6.2 that OPAM assigns tasks’ WCETs to their execution times when it simulates the worst-case executions of tasks while varying task arrival times. In many real-time systems studies (Briand et al. 2005 ; Guan et al. 2009 ; Lin et al. 2009 ; Anssi et al. 2011 ; Zeng et al. 2014 ; Di Alesio et al. 2015 ; Du̇rr et al. 2019 ), static WCETs are often used instead of varying task execution times for the purpose of real-time analysis. For example, practitioners typically use WCETs to estimate the lowest bound of CPU utilization required to properly apply the rate monotonic scheduling policy (Fineberg and Serlin 1967 ) to their systems. Similarly, OPAM assumes that near-worst-case schedule scenarios can be simulated by assigning tasks’ WCETs to their execution times and varying tasks’ arrival times using search. A near-worst-case schedule scenario entails that the magnitude of deadline misses is maximized when tasks execute as per this scenario. Under this working assumption, we were able to empirically evaluate the sanity, coevolution, scalability, and usefulness aspects of OPAM (see Section  7 ). The results indicate that OPAM is a promising and useful tool. However, the formal proof of whether or not the WCET assumption holds in the system model described in Section  3 requires complex analysis, accounting for varying task arrival times, triggering relationships, resource dependencies, and multiple cores. When task execution times need to be varied during simulation, engineers can adapt OPAM by utilizing Monte-Carlo simulation (Kroese et al. 2014 ) to account for such variations.

The main threat to external validity is that our results may not generalize to other systems. We mitigate potential biases and errors in our experiments by drawing on real industrial subjects from different domains and several synthetic subjects. Specifically, we selected two subjects from the aerospace domain, two from the automotive domain, and two from the avionics domain. The positive feedback obtained from LuxSpace and the encouraging results from our industrial case studies indicate that OPAM is a scalable and practical solution. Furthermore, we believe OPAM introduces a promising avenue for addressing the problem of priority assignment by applying coevolutionary algorithms, even for systems that use other scheduling policies, e.g., priority inheritance. In order for OPAM to support different scheduling policies, the main requirement is to replace the existing simulator (described in Section  6 ) with a new simulator supporting the desired scheduling policy. In our approach, the coevolution part of OPAM is separated from the scheduling policy, which is contained in the simulator. Hence, we deem the expected changes for the coevolution part of OPAM to be minimal. Future studies are nevertheless necessary to investigate how OPAM can be adapted to find near-optimal priority assignments for other real-time systems in different contexts.

8 Conclusion

We developed OPAM, a priority assignment method for real-time systems, that aims to find equally viable priority assignments that maximize the magnitude of safety margins and the extent to which engineering constraints are satisfied. OPAM uses a novel approach, based on multi-objective, competitive coevolutionary search, that simultaneously evolves different species, i.e., populations of priority assignments and stress test scenarios, that compete with one another with opposite objectives, the former trying to minimize chances of deadline misses while the latter attempts to maximize them. We evaluated OPAM on a number of synthetic systems as well as six industrial systems from different domains. The results indicate that OPAM is able to find significantly better solutions than both those manually defined by engineers based on expert knowledge and those obtained by our baselines: random search and sequential search. Further, OPAM scales linearly with the number of tasks in a system and the time required to simulate task executions. Execution times on our industrial systems are practically acceptable.

In the future, we will continue to study the problem of optimal priority assignment by accounting for (1) priority assignments that change dynamically, (2) WCET value ranges that account for non-deterministic computation times, (3) interrupt handling routines that execute differently compared to real-time tasks, and (4) hybrid scheduling policies that combine multiple standard policies. We also plan to develop a real-time task modeling language to specify task characteristics such as resource dependencies, triggering relationships, engineering constraints, and behaviors of real-time tasks and to facilitate real-time system analysis, e.g., optimal priority assignment and schedulability analysis. In addition, we would like to incorporate additional analysis capabilities into OPAM in order to verify whether or not a system satisfies the required properties, e.g., schedulability of tasks and absence of deadlocks, for a given priority assignment. For example, statistical model checking (Legay et al. 2010 ) may allow us to verify whether tasks meet their deadlines for a given priority assignment with a probabilistic guarantee. In the long term, we plan to more conclusively validate the usefulness of OPAM by applying it to additional case studies in different application domains.

Bosch GmbH: https://www.bosch.com/

Continental AG: https://www.continental.com

ENSMA: https://www.ensma.fr/

IBM: https://www.ibm.com/

ESA: https://www.esa.int/

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Acknowledgements

We thank Yago Isasi Parache, LuxSpace, for his support in conducting our industrial case study. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 694277), and NSERC of Canada under the Discovery and CRC programs. The experiments presented in this paper were carried out using the HPC facilities of the University of Luxembourg (Varrette et al. 2014 ) – see http://hpc.uni.lu .

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Lee, J., Shin, S.Y., Nejati, S. et al. Optimal priority assignment for real-time systems: a coevolution-based approach. Empir Software Eng 27 , 142 (2022). https://doi.org/10.1007/s10664-022-10170-1

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Understanding Strategic Priorities

Aligning strategic priorities with business goals, balancing short-term and long-term priorities, strategic priority 1: customer focus, strategic priority 2: operational efficiency, strategic priority 3: innovation and growth, strategic priority 4: talent development, strategic priority 5: market expansion, strategic priority 6: sustainability and social responsibility, strategic priority 7: financial performance, strategic priority 8: risk management, strategic priority 9: digital transformation, strategic priority 10: quality assurance, strategic priority examples.

For any resourceful product manager, effective planning and execution are crucial for success. An important aspect of this process is identifying strategic priorities that align with your company goals and roadmap. By establishing clear strategic priorities, you can focus your resources and efforts on the areas that will drive growth and ensure long-term success. In this article, we will explore 10 strategic priority examples that can guide your planning and execution efforts.

Before delving into the specific strategic priority examples, let’s first understand the role that strategic priorities play in business planning. Strategic priorities are the key areas of focus that shape your business strategy and drive decision-making. They provide a roadmap for your organization, outlining the most important objectives that need to be accomplished to achieve your vision.

Strategic priorities serve as a compass, guiding your organization in the right direction. By defining clear priorities, you create a sense of purpose and direction for your team. They help you allocate resources effectively, ensuring that time, money, and effort are invested in the right areas. Strategic priorities also provide a framework for decision-making, allowing you to evaluate opportunities and challenges in light of your long-term goals.

When it comes to effective strategic priorities, certain key elements should be considered. Firstly, they should be aligned with your overall business goals. Each priority should enhance your ability to achieve your vision and mission. For example, if your business goal is to become a leader in sustainable practices, one of your strategic priorities could be to reduce carbon emissions by a certain percentage within a specific timeframe.

Secondly, strategic priorities should be specific and measurable. This allows you to evaluate progress and make adjustments as needed. Instead of having a vague priority like “improve customer satisfaction,” you could have a specific priority such as “increase customer satisfaction ratings by 10% within the next quarter.” This specificity helps you track your progress and determine the effectiveness of your strategies.

Additionally, it’s crucial to involve key stakeholders in the process of defining strategic priorities. By gaining buy-in and input from various departments and team members, you can ensure that your priorities are comprehensive and well-rounded. This collaborative approach not only increases the likelihood of successful implementation but also fosters a sense of ownership and commitment among your team.

Furthermore, strategic priorities should be dynamic and adaptable. In a rapidly changing business environment, it’s important to regularly review and update your priorities to stay relevant and responsive to market trends and customer needs. This flexibility allows you to seize new opportunities and address emerging challenges effectively.

Crafting Your Strategic Priorities

Now that we have a clear understanding of strategic priorities, it’s time to delve into the process of crafting them. Let’s explore two important aspects of crafting effective strategic priorities: aligning them with your business goals and balancing short-term and long-term priorities.

One of the critical factors in creating effective strategic priorities is ensuring they align with your overall business objectives. For example, if your business goal is to increase customer satisfaction, one of your strategic priorities could be “Customer Focus.” By placing a strong emphasis on understanding and meeting customer needs, you ensure that every decision made within your organization is driven by the desire to provide exceptional customer experiences.

When aligning strategic priorities with business goals, it is essential to consider the specific needs and preferences of your target audience. Conducting market research and gathering customer feedback can provide valuable insights that inform your strategic priorities. By understanding your customers’ pain points and desires, you can tailor your priorities to address their needs effectively.

Furthermore, aligning strategic priorities with business goals requires a comprehensive understanding of your organization’s capabilities and resources. It’s crucial to assess your internal strengths and weaknesses to determine which priorities are feasible and realistic. By aligning your strategic priorities with your organization’s capabilities, you increase the likelihood of successful implementation and achievement of your business objectives.

Another important consideration when crafting strategic priorities is striking a balance between short-term and long-term objectives. While it’s essential to focus on immediate goals that drive immediate results, it’s equally important to have a long-term perspective. This balance ensures that your organization remains agile and adaptable to changing market conditions while also working towards sustainable growth and long-term success.

When balancing short-term and long-term priorities, it’s crucial to prioritize your resources effectively. Short-term priorities often require immediate attention and allocation of resources, while long-term priorities may require more significant investments and a longer time frame for implementation. By carefully evaluating the potential impact and return on investment for each priority, you can allocate your resources in a way that maximizes both short-term gains and long-term growth.

Additionally, balancing short-term and long-term priorities involves considering potential trade-offs and risks. Some short-term priorities may have a temporary negative impact on long-term goals, while some long-term priorities may require sacrifices in the short term. It’s essential to evaluate these trade-offs and risks carefully and make informed decisions that align with your organization’s overall strategic direction.

Furthermore, effective communication and collaboration across different departments and teams are crucial for balancing short-term and long-term priorities. By fostering a culture of cross-functional collaboration and encouraging open dialogue, you can ensure that all stakeholders are aligned and working towards a common vision. This collaborative approach helps in identifying potential conflicts or overlaps between short-term and long-term priorities and finding creative solutions to address them.

In conclusion, crafting effective strategic priorities involves aligning them with your business goals and balancing short-term and long-term objectives. By considering the specific needs of your target audience, assessing your organization’s capabilities, and prioritizing resources effectively, you can create strategic priorities that drive sustainable growth and success.

The 10 Strategic Priority Examples

Now that we have explored the process of understanding and crafting strategic priorities, let’s dive into the specific examples. In this section, we will showcase 10 strategic priority examples that can inspire your planning and execution efforts.

Putting the customer at the center of your business operations is a strategic priority that can drive success. By understanding customer needs and delivering exceptional experiences, you build loyalty and create advocates for your brand.

Improving operational efficiency can streamline processes, reduce costs, and increase productivity. By investing in technology and process optimization, you ensure that your organization operates at its full potential.

Innovation and growth go hand in hand. By prioritizing innovation, you create a culture of continuous improvement, leading to new products, services, and opportunities for expansion.

Your employees are your greatest asset. By focusing on talent development, you invest in their growth and ensure your organization has the skills and knowledge needed to thrive in a competitive market.

Expanding into new markets can open up exciting growth opportunities. By identifying potential markets and developing strategies to penetrate them, you can diversify your customer base and increase your revenue streams.

Showing a commitment to sustainability and social responsibility not only helps the planet and communities but also resonates with customers. By incorporating sustainable practices into your operations, you can attract environmentally conscious consumers and differentiate your brand.

Ensuring strong financial performance is crucial for the long-term success of any organization. By prioritizing financial stability, you can make informed decisions, manage risks effectively, and invest in future growth.

Identifying and mitigating risks is a strategic priority that ensures the sustainability of your organization. By proactively assessing potential risks and developing contingency plans, you can navigate uncertainties and protect your business.

In today’s digital age, embracing technology is essential for staying competitive. By prioritizing digital transformation, you can leverage new tools and platforms to streamline processes, enhance customer experiences, and drive innovation.

Quality assurance is paramount for maintaining customer satisfaction and loyalty. By prioritizing quality in every aspect of your operations, you can deliver products and services that meet and exceed customer expectations.

By incorporating these 10 strategic priority examples into your planning and execution efforts, you can ensure that your business is on the right track. Remember, by aligning your priorities with your goals, balancing short-term and long-term objectives, and involving key stakeholders in the process, you set yourself up for success in today’s dynamic business landscape.

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Priority Assignment to Tasks in Operating System

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Assigning priority to tasks : When the number of tasks with different relative deadlines are more than the priority levels supported by the operating system, then some tasks share the same priority value. But the exact method of assigning priorities to tasks can proficiently affect the utilization of processor.

If the tasks are randomly selected for sharing the same priority level then the utilization of the processor would be lessen. It is required to select the tasks systematically to share a priority level so that the achievable schedulable utilization would be higher.

There are several priority assignment methods used when tasks share the same priority level. Some of the most used methods are:

1. Uniform Priority Assignment : In this assignment method, all the tasks are uniformly divided among the available priority levels. If the number of priority levels completely divides the number of tasks then uniform division tasks among priority levels can be done easily.

For example, if there are 20 tasks to be scheduled and 4 priority levels are supported by the operating system then each priority level is assigned 5 tasks. If uniform division is not possible i.e there are N tasks and p priority levels and N % p > 0 then floor(N/p) tasks are assigned to each level and remaining tasks are assigned to lower priority levels.

For example, if there are 10 tasks to be scheduled and 4 priority levels are supported by the operating system then firstly floor(10/4) i.e. 2 tasks are assigned to each priority levels and remaining 2 tasks are assigned to one each to lower priority levels.

2. Arithmetic Priority Assignment : In this assignment method, an arithmetic progression is formed by the number of tasks assigned to each priority level.

For example, If N are number of tasks and p is number of priority levels then

where ‘a’ tasks are assigned to highest priority level, ‘2a’ tasks are assigned to next highest priority level and so on.

3. Geometric Priority Assignment : In this assignment method, a geometric progression is formed by the number of tasks assigned to each priority level.

where ‘a’ tasks are assigned to highest priority level, ‘a^2’ tasks are assigned to next highest priority level and so on.

3. Logarithmic Priority Assignment : In this assignment method, shorter period tasks are allotted distinct priority levels as much as possible and lower priority tasks (tasks with higher period) are combined together and assigned to same priority level so that higher priority tasks would not be affected. For this the tasks are arranged in increasing order of their period.

If p max is the maximum period and p min is the minimum period among period of all tasks and p is the number of priority levels then,

and tasks with periods up to k are assigned to highest priority, tasks with periods from k to k^2 are assigned to next highest priority, tasks with periods from k^2 to k^3 are assigned to next highest priority and so on.

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Priority Scheduling Algorithm: Preemptive, Non-Preemptive EXAMPLE

What is Priority Scheduling?

Priority Scheduling is a method of scheduling processes that is based on priority. In this algorithm, the scheduler selects the tasks to work as per the priority.

The processes with higher priority should be carried out first, whereas jobs with equal priorities are carried out on a round-robin or FCFS basis. Priority depends upon memory requirements, time requirements, etc.

Types of Priority Scheduling

Priority scheduling divided into two main types:

Preemptive Scheduling

In Preemptive Scheduling, the tasks are mostly assigned with their priorities. Sometimes it is important to run a task with a higher priority before another lower priority task, even if the lower priority task is still running. The lower priority task holds for some time and resumes when the higher priority task finishes its execution.

Non-Preemptive Scheduling

In this type of scheduling method, the CPU has been allocated to a specific process. The process that keeps the CPU busy, will release the CPU either by switching context or terminating. It is the only method that can be used for various hardware platforms. That’s because it doesn’t need special hardware (for example, a timer) like preemptive scheduling.

Characteristics of Priority Scheduling

• A CPU algorithm that schedules processes based on priority.
• It used in Operating systems for performing batch processes.
• If two jobs having the same priority are READY, it works on a FIRST COME, FIRST SERVED basis.
• In priority scheduling, a number is assigned to each process that indicates its priority level.
• Lower the number, higher is the priority.
• In this type of scheduling algorithm, if a newer process arrives, that is having a higher priority than the currently running process, then the currently running process is preempted.

Example of Priority Scheduling

Consider following five processes P1 to P5. Each process has its unique priority, burst time, and arrival time.

Step 0) At time=0, Process P1 and P2 arrive. P1 has higher priority than P2. The execution begins with process P1, which has burst time 4.

Step 1) At time=1, no new process arrive. Execution continues with P1.

Step 2) At time 2, no new process arrives, so you can continue with P1. P2 is in the waiting queue.

Step 3) At time 3, no new process arrives so you can continue with P1. P2 process still in the waiting queue.

Step 4) At time 4, P1 has finished its execution. P2 starts execution.

Step 5) At time= 5, no new process arrives, so we continue with P2.

Step 6) At time=6, P3 arrives. P3 is at higher priority (1) compared to P2 having priority (2). P2 is preempted, and P3 begins its execution.

Step 7) At time 7, no-new process arrives, so we continue with P3. P2 is in the waiting queue.

Step 8) At time= 8, no new process arrives, so we can continue with P3.

Step 9) At time= 9, no new process comes so we can continue with P3.

Step 10) At time interval 10, no new process comes, so we continue with P3

Step 11) At time=11, P4 arrives with priority 4. P3 has higher priority, so it continues its execution.

Step 12) At time=12, P5 arrives. P3 has higher priority, so it continues execution.

Step 13) At time=13, P3 completes execution. We have P2,P4,P5 in ready queue. P2 and P5 have equal priority. Arrival time of P2 is before P5. So P2 starts execution.

Step 14) At time =14, the P2 process has finished its execution. P4 and P5 are in the waiting state. P5 has the highest priority and starts execution.

Step 15) At time =15, P5 continues execution.

Step 16) At time= 16, P5 is finished with its execution. P4 is the only process left. It starts execution.

Step 17) At time =20, P5 has completed execution and no process is left.

Step 18) Let’s calculate the average waiting time for the above example.

Waiting Time = start time – arrival time + wait time for next burst

Advantages of priority scheduling

Here, are benefits/pros of using priority scheduling method:

• Easy to use scheduling method
• Processes are executed on the basis of priority so high priority does not need to wait for long which saves time
• This method provides a good mechanism where the relative important of each process may be precisely defined.
• Suitable for applications with fluctuating time and resource requirements.

Disadvantages of priority scheduling

Here, are cons/drawbacks of priority scheduling

• If the system eventually crashes, all low priority processes get lost.
• If high priority processes take lots of CPU time, then the lower priority processes may starve and will be postponed for an indefinite time.
• This scheduling algorithm may leave some low priority processes waiting indefinitely.
• A process will be blocked when it is ready to run but has to wait for the CPU because some other process is running currently.
• If a new higher priority process keeps on coming in the ready queue, then the process which is in the waiting state may need to wait for a long duration of time.
• Priority scheduling is a method of scheduling processes that is based on priority. In this algorithm, the scheduler selects the tasks to work as per the priority.
• In Priority Preemptive Scheduling, the tasks are mostly assigned with their priorities.
• In Priority Non-preemptive scheduling method, the CPU has been allocated to a specific process.
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