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The American Association of Insurance Services partnered with IBM to create an open blockchain network that streamlines regulatory reporting. The network provides new insights for insurers, while also enhancing timeliness, accuracy and value for regulators.

With IBM Cloud bare metal server infrastructure hosted worldwide and the Veeam on IBM Cloud backup and restore service, Movius provides its multiline offering to enterprises around the globe.

To transform its service desk operations, banking group Creval deployed a virtual assistant, powered by IBM Watson technology, that reduced service desk calls by 80 percent and allowed staff to focus on high-value tasks that drive customer service excellence.

To help ensure clients can access the latest pricing and execute trades rapidly and accurately, online broker ActivTrades migrated its primary trading platforms from on-premises infrastructure to IBM Cloud for VMware solutions.

Allianz wanted a mobile assistant solution that worked across platforms to better serve customers. Using IBM Cloud and IBM Watson Assistant, the company created an AI-powered virtual assistant that can field 80 percent of its most frequent customer requests — for real help in real time.

IBM is helping American Airlines migrate some of its critical applications to the IBM Cloud while using new methodology to create innovative applications quickly and improve the customer experience.

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To help state healthcare agencies address new regulations, Deloitte teamed with IBM to develop the Medicaid Enterprise Solution (MES) HealthInteractive Platform, running on IBM Cloud technology. With MES, state Medicaid programs can keep IT up to date with less effort and expense, while also aligning with federal guidance.

A Japanese airline has become the first among its competitors to develop a web-based chatbot proof of concept, generating real dialogs with 90 percent of users and confirming the company’s vision for broader cognitive applications with IBM Watson Assistant (formerly IBM Watson Conversation) and Watson Natural Language Classifier services.

Active International used the IBM Cloud to develop applications that optimize its media buying cycle and help it identify new business prospects more effectively.

Anthem partnered with IBM to drive its digital transformation and deliver an enhanced digital experience for its nearly 40 million consumers.

Assima helps employees work smarter by arming them with an intelligent application overlay hosted in the IBM Cloud and delivering AI capabilities with IBM Watson technology.

To help small and midsized businesses with liquidity management and planning, Asteria created a smart cashflow service running on scalable IBM Cloud infrastructure. With its IBM Cloud solution, the company can work in a flexible, open-source development framework while also addressing customer needs for security-rich data hosting.

Buzz Radar created a platform called the Cognitive Command Center — a digital marketing monitoring, analytics and visualization platform that harnesses IBM Watson technologies and runs in the IBM Cloud.

After working with IBM to build a cloud data warehouse and analytics architecture, Constance Hotels, Resorts & Golf can now gain data-driven insights from across its nine properties.

Building on a long track record of service innovation, ExxonMobil engaged an IBM iX team to help design and launch ExxonMobilRewards+, the industry’s first smartphone app for digital payment at the pump. The solution’s IBM Cloud public hosting platform reduces operating costs for the app by 40 percent and scales effortlessly as its user base continues to grow.

LogDNA saw a clear need to address data sprawl in the modern, cloud-native development stack. Its innovative software-as-a-service (SaaS) platform built on Kubernetes caught the attention of the IBM Cloud team, which wove it into its global framework. As both an IBM Business Partner and a client, LogDNA was able to grow and strengthen its DevOps capabilities.

Ricoh migrated its Unified Communication System operations to IBM Cloud bare metal servers, taking advantage of near-limitless scalability and capacity. This allowed the company to ensure smooth sound and vision for its rapidly expanding customer base.

UBank launched new initiatives in an IBM Cloud Platform environment, including a virtual assistant that incorporates IBM Watson technology to support the bank’s online home loan application.

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MOBILE CLOUD COMPUTING: CASE STUDIES

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Vinod Pachghare

Now a day use of online trading and ecommerce are increasing rapidly. Use of Internet is also increased day by day. Most of the people use Internet based computing called cloud computing. In cloud computing software and hardware resources are available to the users as per their demand. Remote computing sites can use these resources easily with the help of Internet. Cloud computing allows the users to share the infrastructure, storage and computing resources. This helps to reduce the cost of the various applications. Cloud computing uses distributed network to provide different services and applications to the users. It also support for virtualized resources. To adopt cloud by maximum number of people mobility support is very important. But mobile devices have some limitations. Some of the major limitations of mobile devices are bandwidth limitations, battery lifetime and small storage. Use of cloud with mobile devices can solve these problems

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Cloud computing is becoming an increasingly popular enterprise model in which computing resources are made available on-demand to the user as needed. The unique value proposition of cloud computing creates new opportunities to align IT and business goals. Cloud computing use the internet technologies for delivery of IT-Enabled capabilities 'as a service' to any needed users i.e. through cloud computing we can access anything that we want from anywhere to any computer without worrying about anything like about their storage, cost, management and so on. In this paper I provide a comprehensive study on the motivation factors of adopting cloud computing, review the several cloud deployment and service models. It also explore certain benefits of cloud computing over traditional IT service environment-including scalability, flexibility, reduced capital and higher resource utilization are considered as adoption reasons for cloud computing environment. I also include security, privacy, and internet dependency and availability as avoidance issues. The later includes vertical scalability as technical challenge in cloud environment.

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Current: Preparing teenagers for financial responsibility

About Current

Current is a financial technology company that offers a debit card and app made for teenagers. The app and card give teens hands-on learning with modern financial tools, and connects them with the people, brands, and experiences they value.

Tell us your challenge. We're here to help.

Current uses google kubernetes engine on google cloud to improve time to market for app development by 400% while eliminating downtime for users of its debit card app., google cloud results.

• Improves time to market for app development by 400%
• Eliminates downtime for customers
• Enables deployment of new services in hours versus days
• Reduces total cloud hosting costs by 60%

80% reduction in error resolution time

When it comes to developing good financial habits, it pays to start early. Talking to teens about money and monitoring how they spend it helps set them up for a more financially sound future and can have long-term implications for the rest of their lives.

Instead of handing teens cash, many parents are using Current , a Visa chip debit card and smartphone app that helps teens learn how to budget money. Teens can set savings goals, check their balances, earn money by completing chores, and even give to charity. Parents can set an automated allowance, create and approve chores, and easily track their children’s spending with real-time alerts.

To grow, Current must keep its app secure, reliable, and high performing. As a startup, the company started by developing and hosting its app on a simple infrastructure, managing virtual machines with manual processes. As its user base surpassed 25,000 daily active customers, Current began to notice performance bottlenecks, particularly with the Neo4j graph database it uses to store and expose relationships among users, family members, and their debit cards and connected banks. Running the database on a shared application server made it difficult to measure the cost of the required CPU time and memory footprint. Current also lacked a robust way to log and profile the database.

Current considered using a hosted Neo4j solution, but worried that it would limit its ability to deploy in different availability zones as the company grew. Current was also concerned that a hosted solution would drastically increase costs.

“Since moving to Google Cloud, we’ve been able to sustainably grow our user base 7x to more than 175,000 users, and we haven’t experienced any downtime for our services. We’ve also received a lot of collaboration and support from Google, which we weren’t getting from other cloud providers.”

After a short stint with another cloud provider, Current decided to build its own graph database cluster on Google Cloud . The highly available implementation—including a monitoring agent and backup agent—came in at half the cost of a hosted solution or alternative cloud provider according to Trevor Marshall, Chief Technology Officer at Current. Once the engineering team saw the power and reliability of Google Cloud, Current began exploring deeper integration with Google Cloud services.

“Since moving to Google Cloud, we’ve been able to sustainably grow our user base 7x to more than 175,000 users, and we haven’t experienced any downtime for our services,” says Trevor. “We’ve also received a lot of collaboration and support from Google, which we weren’t getting from other cloud providers.”

Accelerating time-to-market

Current now hosts most of its applications in Docker containers, including its business-critical GraphQL API, using Google Kubernetes Engine to automate cluster deployment and management of containerized applications while keeping applications available. Container images are stored on Google Container Registry for fast, scalable retrieval. Integrated logging with Google Stackdriver makes it easy to identify issues, and Current can scale up or down as needed to keep performance high and costs low, with zero downtime for users.

“Moving to Google Cloud reduced our error resolution times by 80% and improved our time to market for app development by 400%. We can iterate quickly, find issues, and redeploy. There’s no reason whatsoever to run Kubernetes outside of Google Cloud, because Google does such a good job.”

With a fully managed environment for containerized applications, Current can deploy new services in hours instead of days while keeping its staffing footprint small. When the company does add team members, they can focus on app development instead of managing and troubleshooting infrastructure.

“Moving to Google Cloud reduced our error resolution times by 80% and improved our time to market for app development by 400%,” says Trevor. “We can iterate quickly, find issues, and redeploy. There’s no reason whatsoever to run Kubernetes outside of Google Cloud, because Google does such a good job.”

Current has released a variety of compelling new features since moving to Google Cloud, including a referral program to recruit more customers and an improved notification feed to inspire more conversations about finances between parents and teens. It also restructured its app to highlight users’ favorite features, including a dedicated allowance section and improved chore management. The new app also communicates with Current’s Kubernetes Engine hosted GraphQL API. Current’s use of GraphQL greatly improves performance by minimizing the data that is sent between the app and the backend, and enables Current’s front-end engineers to share code, increasing developer efficiency.

Improving data and network security

As a financial technology company, Current is always focused on providing the highest levels of security for its customers. Google Cloud facilitates the use of encryption to help protect customer data at rest and in transit to help ensure that customer data is safe when outside the physical boundaries not controlled by Google or on behalf of Google.

For publicly accessible applications, Current configures an ingress resource on Kubernetes clusters to make context-aware load balancing decisions. This ingress also provides a reverse proxy function between users and Current's private network. This helps ensure that no external entity can reach Current’s Google Compute Engine instance fleet directly. Google Cloud also provides Current with the means to forward traffic outside of its private without exposing instances to the public Internet. This gives Current the means to utilize other managed services such MongoDB Atlas, while maintaining a trusted platform.

“Security is one of the biggest benefits of Google Cloud and Kubernetes Engine,” says Trevor. “It was easy for us to configure our environment so that we avoid exposing any public IP addresses for our clusters. When we deploy a new service, we have a recipe that observes security best practices.”

“Google Cloud has allowed us to be highly available, scalable, and cost-efficient, helping us grow from an ambitious startup into a financial technology innovator. We’ve built trust with the families we serve because we’ve been able to offer a great experience.”

Powering a digital workforce

When Current was founded in 2015, the company standardized on Google Workspace for communication and collaboration, using tools such as Gmail and Google Docs , Sheets , and Slides to keep productivity high. Google Workspace administration is so easy that Trevor still handles it all, in addition to leading the company’s tech strategy as CTO.

“Our business depends on Google Workspace,” he says. “It’s simple to use, yet feature-rich and very cost effective. Adding new employees takes a couple of minutes, and they can get to work right away. I can’t imagine using anything else.”

Shaping financial futures

By making it easy for teens and parents to manage and talk about money, Current is preparing a new generation to navigate one of the most challenging aspects of adulthood: financial responsibility. The company’s user base is growing by 20% every month with no signs of slowing, and its Android app just began trending on Google Play. Current is also learning to better manage its own finances. “By avoiding the cost of a hosted Neo4j solution and optimizing resource utilization with Kubernetes Engine, we reduced total cloud hosting costs by 60%," adds Trevor.

“Google Cloud has allowed us to be highly available, scalable, and cost-efficient, helping us grow from an ambitious startup into a financial technology innovator,” says Trevor. “We’ve built trust with the families we serve because we’ve been able to offer a great experience.”

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Mobile Cloud Computing

The Mobile Cloud Computing project looks at architectures and protocols of next generation infrastructures that exploit the synergy between Mobile devices, Internet of Things (IoT) devices, and Cloud Computing. It develops answers to how to enable new classes of CPU-intensive, and data-intensive, applications for mobile devices and how to process large number of real-time concurrent interactive data streams emerging from the IoT environment. Research areas of interest include formal methods, Operating Systems, Virtualization, and IP-based and Information Centric Networking protocol stacks for resource-constrained environments. Undergoing efforts are summarized below.

Description

Design robustness using formal language 

This effort develops a formal specification using the π-calculus to define a virtual device representation. It also describes a way to compose multiple virtual devices representing physical devices available on the network to build a composite virtual device.  During this process we address the offloading of applications running on virtual devices to local clouds (Cloulets). The proposed 3-tiered (Mobile device, Cloudlet, and Public Cloud) architecture develops a framework to integrate them and case studies to show the structural congruence between a locally executed application and an offloaded version of the same application.

Continuous Monitoring  

This effort builds on the previous architecture to add continuous performance monitoring from the device perspective. The focus is on collecting data that will supply additional information to improve the performance this dynamic, distributed and real-time nature of the architecture. 

Protocol for the Interoperability 

The application offloading concern is a complex problem which contains communication, application isolation, and persistence layers. We focus on the first layer – Mobile Offloading Communication Protocol (MOCP). This is a communication protocol between the cloudlet which plays the server role and the mobile application manager which plays the client role. The manager pilots the whole life cycle of the mobile application on the mobile device. An Application Program Interface (API) is built on top of Representational State Transfer (REST) that enables the automatic generation of MOCP’s skeletons for servers and mobile devices in multiple programming languages such as Java, C++ and JavaScript.  

Major Accomplishments

• Definition of the Mobile Cloud architecture using formal methods
• Test method for the robustness of the offloading using the structural congruence
• Device virtualization and composition for both mobile devices and IoT devices
• Performance monitoring for the Mobile Cloud 
• Mobile Offloading Communication Protocol (MOCP)

Associated Product(s)

Publications:

• Towards a Formal Definition of the Mobile Cloud
• Monitoring Architecture for Cloudlet-Based Mobile Cloud Computing

• DOI: 10.1109/WAINA.2014.22
• Corpus ID: 7548028

Real-Time Mobile Cloud Computing: A Case Study in Face Recognition

• Marwa Ayad , M. Taher , Ashraf M. Salem
• Published in 28th International Conference… 13 May 2014
• Computer Science
• 2014 28th International Conference on Advanced Information Networking and Applications Workshops

Figures from this paper

26 Citations

A survey on face recognition through mobile cloud computing environment, design and development of a parallelized algorithm for face recognition in mobile cloud environment, the case of face recognition on mobile devices, dynamic tasks assignment for face recognition in edge computing, smartlet: a dynamic architecture for real time face recognition in smartphone using cloudlets and cloud, fog computing based face identification and resolution scheme in internet of things, a unified face identification and resolution scheme using cloud computing in internet of things, intelligent file transfer for smart handheld devices based on mobile cloud computing, a new energy-preserving cloud offloading algorithm for smart mobile devices, cloud-based face and speech recognition for access control applications, 29 references, gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications, a survey of mobile cloud computing: architecture, applications, and approaches, mobile cloud computing: a survey, the case for vm-based cloudlets in mobile computing, hyrax: cloud computing on mobile devices using mapreduce, comparative study of cloud computing and mobile cloud computing, cloud computing for mobile world, evaluation of haar cascade classifiers designed for face detection, eigenface-based facial recognition, maui: making smartphones last longer with code offload, related papers.

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An archetypal determination of mobile cloud computing for emergency applications using decision tree algorithm

• Tao Hai 1 , 2 ,
• Jincheng Zhou 1 , 3 ,
• Dayang N. A. Jawawi 2 ,
• Dan Wang 3 , 5 ,
• Shitharth Selvarajan 6 ,
• Hariprasath Manoharan 7 &
• Ebuka Ibeke 8  

Journal of Cloud Computing volume  12 , Article number:  73 ( 2023 ) Cite this article

2209 Accesses

9 Citations

Metrics details

Numerous users are experiencing unsafe communications due to the growth of big network mediums, where no node communication is detected in emergency scenarios. Many people find it difficult to communicate in emergency situations as a result of such communications. In this paper, a mobile cloud computing procedure is implemented in the suggested technique in order to prevent such circumstances, and to make the data transmission process more effective. An analytical framework that addresses five significant minimization and maximization objective functions is used to develop the projected model. Additionally, all mobile cloud computing nodes are designed with strong security, ensuring that all the resources are allocated appropriately. In order to isolate all the active functions, the analytical framework is coupled with a machine learning method known as Decision Tree. The suggested approach benefits society because all cloud nodes can extend their assistance in times of need at an affordable operating and maintenance cost. The efficacy of the proposed approach is tested in five scenarios, and the results of each scenario show that it is significantly more effective than current case studies on an average of 86%.

Introduction

In this paper, we implement a mobile cloud computing procedure in the proposed technique in order to prevent unsafe or difficulty in communication as a result of the growth of big network mediums. The proposed technique uses a machine learning method known as Decision Tree optimization algorithm with a number of subset features, and indulgent network information totally removes the impure cloud computing nodes from the network.

The following are the major contributions of this paper:

An efficient simulation model is used to demonstrate how mobile cloud computing might be used to handle various emergency situations.

The cloud nodes in the proposed approach can extend their assistance in times of need at an affordable operating and maintenance cost.

The mobile cloud computing nodes are designed with strong security, ensuring that all the resources are allocated appropriately.

The remainder of this paper is organized as follows: Literature survey section provides the literature survey. In Mobile computing: system model section, the mobile computing system model is presented. Optimization algorithm  s ection presents the optimization algorithm. In  Experimental verification  section, an experimental verification of the proposed models and algorithms is carried out. Finally, Conclusion section concludes the study and provides the future scope.

Literature survey

This section looks at the methods for making fundamental decisions in order to help readers comprehend the many difficulties that arise when designing mobile computing platforms. Since not all data is sent in a consistent manner, users are unable to create computing networks in a common way. Therefore, in order to create a mobile computing network, devices must be tested before installation, which leads to a number of problems that must be analyzed. The proposed method is then put into practice to address some of the most significant shortcomings in the current system. Using differential equations, the computational procedure in [ 1 ] analyses the random nodes, leading to the formation of numerous straightforward expressions. A stationary distribution of mobile nodes with significant physical space is expressed by the simple expression form. However, the model of expression is different because the intended system lacks dimensions but still produces exponential case studies. In [ 2 ], a hypothetical framework was offered for making judgments using both an ideal and an adversarial mode of operation in order to provide a multidimensional model. The entire setup is accelerated since the appropriate mode of operation is offered, and the results are significantly higher than anticipated. However, the best method of data segmentation minimizes the latency duration of mobile computing processes. The computing procedure is introduced even on other application platforms [ 3 ], where emergency medical centres are identified by mobile locations. If the computing process is only focused on mobile locations, then a certain computing channel will experience significant traffic, increasing the design's complexity.

A system architecture was designed to use sensing techniques to make mobile computing tasks less complicated in [ 4 ]. The aforementioned technique effectively uses a code to link source mobile networks with destination mobile networks. Even though persistent models are utilized to detect feedback in such situations, steady state operation demand is still in inactive mode. More energy is given [ 5 ] to mobile computer nodes in order to activate steady state networks in the system, and this case can only be applied to emergency management systems. If the mobile computer system is used in emergency situations, all system technologies will significantly advance and heterogeneous functioning will not be guaranteed. These systems are entirely avoided because if heterogeneous operations are to be formed, network connectivity must be tested in every emergency situation, which is often not practicable. Given that mobile computing nodes are placed with high handover in numerous locations, adaptive strategies are also offered for various mobile devices to govern handover across the entire operation [ 6 ]. However, if the handover is greater, a specific cell region's analytical model needs to be defined. Utilizing queuing systems, the behavior of the model is examined, and it is shown that the overall handover of the computing process is decreased. However, in a real-time process, complete handover can be minimized in specified circumstances. Initial identification methods are also made utilizing edge computing techniques, and they are used with fifth generation networks in the transportation sector [ 7 ]. Even though a new computing paradigm has been defined, most apps don't work with modernized mobile computing platforms.

In [ 8 ], an inter-cloud system was designed for providing transfer functions during offloading calculations to address these issues. All fundamental criteria in a multi-cloud environment will be evaluated by the transfer function that is introduced for offloading situations. In contrast, the multi-cloud environment for online systems is far more user-defined, necessitating thorough analysis. Cellular networks will offer more flexibility for end-to-end operations if device-to-device contact is developed rather than employing a transfer function. In computing networks, even device-to-device communication is permitted, and a data survey is conducted in this kind of operation [ 9 ] to provide details on discovery and route processing strategies for various cloud computing nodes. Although the method of device communication enables users to accurately access all computing nodes, it neglects to look into the standardization efforts of newly launched mobile nodes. The basic architectural paradigm of cloud computing and information systems is also used to take additional measures for managing emergency systems with mobile computing nodes [ 10 ]. When the process is examined, it becomes clear that the architecture of mobile nodes must be modified at the appropriate times since power distribution across defined networks is improper.

A performance evaluation model was developed to assess the maximum loss in the entire network with optimal resource allocation [ 11 , 12 , 13 ] in order to allocate the right amount of power in the network. However, because the decision-making process in cloud computing is so complex, the greatest degree of loss is seen there. So, after looking at the problems with different approaches, an analytical model is used to come up with a solution that allows all cloud computing operations to be used in emergencies in the best way possible [ 14 , 15 , 16 ]. In [ 17 ], the authors proposed an algorithm for the security and privacy of health information making use of the Modular Encryption Standard (MES). The authors in [ 18 ] presented an offloading technique for multi-access edge computation in a high demand Internet of Things network for energy efficiency and consumption reduction. In [ 19 ], the authors proposed a trust-based routing protocol to enhance the performance and quality of service of the Mobile Ad-hoc Network (MANET). The authors in [ 20 ] introduced an energy efficient architecture based on the cloud and the Internet of Everything (IoE) to optimize the consumption of energy and reduce data traffic. In [ 21 ], the authors designed technology for data privacy in the IoT based on the cloud technology, which hides the data transmission information between the edge servers and the cloud. The proposed algorithm can be modified to deal with real-time applications using edge computing as explained in [ 22 ].

Mobile computing: system model

Given that both the transmitter and receiver must be integrated into the closed loop system, the mobile computing process needs to be assessed using an analytical model. For the purpose of computing proper channel processing in all system formations, the surrounding environments are also examined in this factor. Without an analytical model, random transmission occurs, making it impossible for users to recognize sent packets—even in an emergency situation. Additionally, each packet in the suggested method has a security model established [ 23 , 24 ] allowing for improved resource management during emergency situations. So, using Eq. ( 1 ), here is how you can figure out how much energy each packet uses:

\({P}_{p}\) , \({T}_{p}\) denotes power and time periods of processing units respectively.

According to Eq. ( 1 ), energy usage must be reduced by giving each packet node in the network an equal amount of electricity. The steps involved in giving a specific node packet power can be broken down into the following categories as given in Eq. ( 2 ):

\({r}_{p}\) , \({b}_{p}\) denotes rename and branch powers respectively,

\({w}_{p}\) , \({q}_{p}\) describes window and queue powers for different loads in the packets respectively.

Mobile devices compute the energy based on the Signal-to-Noise Ratio (SNR), which must be minimized in addition to varied energy constraints and the energy consumption during input and output operation changes.

\({\delta }_{i}\) denotes the threshold level of transmitting and receiving devices,

\({O}_{1}\) , \({O}_{i}\) represents first and last observation periods,

\({g}_{i}\) describes gain of computing process.

The SNR, where the second objective function is framed by observing the gain of the mobile computing process, is denoted by Eq. ( 3 ). The suggested method establishes a framework to avoid SNR in the computation process by minimizing the number of loads in the network as given in Eq. ( 4 ) below:

\({\rho }_{i}\) indicates weight of the network packets in computing process,

\({\omega }_{i}\) denotes the number of rationalized packets in the network.

Equation ( 5 ) shows that the packet nodes of all programmes used in mobile computing are based on external weighing variables. This is different from Eq. ( 4 ), where the load depends only on the internal weight of the system.

\({\vartheta }_{l}\) , \({\vartheta }_{hand,out}\) describes the number of restrictions in the server.

Implementing hand-in and out systems in server configuration will help to reduce the number of limits in mobile computing processes. Therefore, by utilizing slot configuration, which is defined by Eq. ( 6 ), the computing effort in the transmission process must be raised as follows:

\({\gamma }_{i}\) denotes attempt of transmission in computing process,

\({c}_{i}\) indicates collision of packets during transmission.

Because nodes may become inactive during the transmission process in the event of a collision, the active period of nodes is determined using Eq. ( 7 ) as shown below:

\({If}_{i}\) denotes operating frequency of nodes,

\({sn}_{i}\) indicates sensing units of connected network.

Optimization algorithm

An algorithm's optimization section is utilized to establish where the appropriate models should be situated in relation to analytical models that are constructed for computing categories. The proposed method integrates a portion of an Artificial Intelligence (AI) algorithm utilizing Machine Learning (ML) techniques since mobile computing [ 25 , 26 ] needs to be determined as an automatic process. The main benefit of using machine learning as an optimization technique is that it allows for accurate computation using modern mechanisms that make all historical data readily visible after undergoing any number of intricate processing steps. The learning method used in this procedure is known as supervised learning since the computing process automatically trains the specified network. Such a learning process begins with making specific decisions only on the basis of trial and error. As a result, if the computing process fails, the best knowledge about the failure instances may be obtained. In the following sequence, the computing process avoids such cases. In order to handle the challenges of computing classification and regression problems, the proposed method is also employed with a decision tree contrivance. It is indicated that only during the initial stages of the decision tree will the whole training data be taken into account, whereas following the evaluation of a specific stage, feature values are preferred, and they are ordered in a specific order. The aforementioned structures enable continuous operation of the prediction process, storing all attribute values during transmitter and receiver computation. The main benefit of decision trees is that all internal mobile nodes are ordered in accordance with a set of attribute systems, which are described in the following mathematical form:

\({\Delta }_{i}\) indicates the probability of sample computing matrix.

The process of mobile computing requires that gain must be maximized with a set of positive values. Therefore, the gain of two different subsets are formulated using Eq. ( 8 ) as follows:

\(\left\{{s}_{i},\left.s\right\}\right.\) indicates subset of entropy functions.

The gain can also be measured using set of mobile classes with index terms and it is represented using Eq. ( 10 ) as follows:

In the proposed method, it is not possible to observe the values of mobile nodes under the same category. Thus, it is essential to provide a necessary split up model using the decision tree process. The split ratio for mobile computing can be formulated using Eq. ( 11 ) as follows:

Where, \({\sigma }_{i}\) describes mobile node information that is separated across different networks.

The mobile nodes will have impure data in the system, thus, it needs to be removed (Fig.  1 ). The analytical model for identifying the impure data is formulated using Eq. ( 12 ):

Flowchart of mobile computing using decision tree

\({\varphi }_{i}\) indicates organization category of computing nodes,

\({pure}_{i}\) describes available data in pure arrangements.

Algorithm 1.  Decision Tree Optimization (DCO)

Experimental verification

The analytical model, which is referred to as a functional aspect of mobile computing, is examined and validated in order to demonstrate its efficacy in actual implementation scenarios. A loop-based system is used to create all analytical equations, and a separate hardware setup device is set up. The initial stage is completed with the presumption that all of the solutions are time-invariant. In order to prevent frequent path breaks in computing systems, additional time durations are taken into consideration once some transmission stages are finished. With more than 670 mobile cloud computing devices taking into account various routes, the suggested technique is examined clearly in both transmission and reception scenarios. Each mobile cloud computing device is built with the ability to send data to a destination and store that data in the cloud with encrypted codes. A unique cloud platform with network connectivity is established for each mobile computing setup using the node installation technique. Therefore, once the data transmission is finished, the nodes that are deployed in the cloud can transition to unload. When using the suggested method for mobile computing cases to look at every possible way to send data, the following things are taken into account:

Scenario 1: Energy of computing nodes

Scenario 2: Signal to Noise Ratio

Scenario 3: External weighting factors

Scenario 4: Activation periods of mobile nodes

Scenario 5: Gain of decision tree

With the help of the MATLAB toolbox, all the aforementioned scenarios are directly simulated, and the programmability of mobile nodes is also examined. In order to demonstrate the effectiveness of the design, it is compared to the existing methods that are applied to the identical scenarios. The integrated hardware arrangement includes a wireless module with extra battery life that is not required throughout the entire network. Thus, in a network where only stable functioning is guaranteed, network complexity is fully decreased. Some adjustments are made manually to ensure stable functioning since resources must be correctly distributed throughout the system. The scenarios are described below.

Scenario 1: energy of computing nodes

Different types of mobile computing systems are used, and each form requires the integration of a certain number of mobile nodes in order to provide the receiver with the proper data transfer. In order to define the processing units, the amount of power supplied to mobile nodes must be adjusted appropriately. Additionally, the amount of time needed to process each unit must be reduced while using a finite amount of power. If both are increased, each node will be represented by a different energy. The aforementioned energy representation example necessitates careful testing because in an emergency, mobile nodes must be energy source error-free. As a result, the suggested solution uses the rename, branch, window, and queue powers in turn to equally distribute the energy allocated to mobile nodes. A significant power supply unit is set aside in addition to all the other power sources as a secondary backup during transmission times. The amount of energy delivered to mobile nodes is seen in Fig.  2 .

Energy measurements of computing nodes

According to Fig.  2 and Table 1 , mobile nodes receive 5.69, 7.23, 9.02, 11.56, and 13.28 watts of power apiece, with corresponding time periods of 1.6, 2.3, 2.9, 3.4, and 3.8 for each amount of power delivered. The energy usage is monitored and compared with existing models for these supply power and time periods [ 6 ]. It is obvious that the suggested approach uses less energy to operate, and even when using minimal energy, the data is successfully delivered to the target location. However, the energy of mobile computer nodes is only partially optimized by the methods now in use, leaving more energy untapped. This can be demonstrated by the 9.02 mill watts of power that is delivered to each CPU node every 2.9 s. This period sees the suggested method's energy consumption drop to 0.05 from the old method's 0.6. Hence, it is important to avoid this type of energy increase because it will seriously harm all of the network's computing nodes.

The Signal-to-Noise Ratio (SNR) is derived using threshold levels and observation times to improve quality in mobile computing nodes. The device operates at a low noise factor thanks to the analytical model that is created for SNR in the suggested technique. In comparison to systems with high noise factors, the complete system will deliver higher computing quality due to its low noise factor. Threshold levels are measured in this scenario for both transmitting and receiving devices, and they must be within 0 decibel points. When a device has a high noise factor, the SNR for recognizing the device grows exponentially. The observation periods are measured in order to determine whether the noise level has increased. Any one observation period is then used for delivery cases. When better network services are offered at a high percentage of times, the chosen observation periods are then separated from the gain values. The comparison values of SNR for the computation process are shown in Fig.  3 .

Signal to noise ratio with threshold factor

Figure  3 and Table 2 shows that the respective threshold values for mobile computing nodes are 7.24, 9.23, 12.45, 13.92, and 14.36. The first and last observation periods, which vary at different step sizes, are discovered to be 10 and 24 using the aforementioned threshold values. The number of observation periods can be thought of in a random manner, where benefit is maximized as a result of such changes. Additionally, the suggested method isolates the prescribed values when the gain is maximized, resulting in the achievement of acceptable SNR values. It is also practical that the suggested method provides low noise levels for the whole observation period, even if it exceeds the required intervals. With threshold values of 12.45 and observation periods of 16, it is evident from the comparison scenario that the proposed technique's SNR is significantly lower than the existing method's. In this situation, the SNR is 1.4 decibels for the existing method and 0.3 decibels for the projected approach.

Scenario 3: external weighting factors

Since the SNR for each connected mobile node can be directly reduced, this scenario offers experimental results based on load allocation because the SNR is reduced but cannot be entirely avoided in the network system. For the experimental scenario, the system rationalises the packets using both internal and external weighting factors. By avoiding the SNR at each network, the weight of specific networks is measured, and because of this, resources are not distributed equally in this situation. Although a separate protocol is not created in the proposed method, some server constraints are also reduced to some extent. The mobile computing nodes are defined in each slot because there is no set of regulations, and the total weight of the slots is taken into account. The system's total number of nodes is looked at and compared. The results are shown in Fig.  4 as hand and out weights, which are said to be the main cause of an increase in external weights.

Total loads with restrictions

According to Fig.  4 and Table 3 , it is reasonable to assume that packet weights are distributed at factors of 4.16, 6.94, 9.01, 13.57, and 14.21, respectively, and that the number of limitations varies for each weight factor in steps of 1, as 2, 3, 4, and 5. The limitations are applied continuously because at certain stages of cloud computing, a higher restriction would force network packets to maintain a steady state. Even further restrictions would cause the entire network to degrade from a point when load is not required. There is a slight increase in weights determined relative to initial determinations in this comparison case where individual weight factors of packets are shown to be in addition to total weight. The various limits on the data transit paths are evident from the 14.21 g of weight per packet. In the case of the projected method using the appropriate decision tree, these constraints result in a weight reduction of the packet of 0.71 g. However, the weight can only be reduced by 1.03 g with the current system, so decisions are not being made properly.

Scenario 4: activation periods of mobile nodes

The proposed approach is constructed in such a way that all nodes cannot be manipulated simultaneously. When connecting distinct cloud computing nodes utilizing onboard processes, it is required to activate each node using separate frequencies in order to increase its performance. When only one cloud computing node is present, figuring out the nodes' activation times is significantly easier. Additionally, the network operation requires that the cloud sensing units always be connected in a fashion that permits sharing of the available frequencies. However, the projected technique makes it much more difficult to share the frequency of operating numerous nodes at once since the introduction of the decision tree procedure. The remaining frequencies are thus fully occupied by following cloud computing nodes in flexible circumstances. Because of what was said above, only the strategy shown in Fig.  5 makes the most of the active periods.

Activation period of computing nodes

Figure  5 and Table 4 show that the activation duration of cloud computing nodes is maximized for the suggested strategy due to a number of factors. For the purposes of the verification scenario, frequency changes of 8.12, 12.25, 16.79, 21.34, and 25.5 MHz are taken into consideration with sensing units of 4, 8, 12, 16, and 20 accordingly. In each instance, the appropriate activation periods are chosen by duplicating each frequency term with the appropriate number of cloud sensing units. When 16.79 MHz is taken into account, 12 sensing units are found, and all activation periods must be larger than 1. The fact that the projected technique reproduces the activation period starting at 1 s shows that all relevant parameters have been examined and the frequency is being delivered in the right manner. However, the activation period is decreased by the current method [ 5 ] even though relevant components have been tested due to poor frequency allocation and sharing. This may be demonstrated using the aforementioned frequency and sensing units, where computing nodes activate every 2.3 s and existing cloud node segments activate at most once every 0.9 s.

Scenario 5: gain of decision tree

The proposed method is employed to express a large number of external parameters and computing node attributes as a subset in order to calculate the gain of the integrated optimization process. In mobile cloud computing, the entropy values are tested using expectation probability values, which are explicitly determined using logarithmic values, to ascertain the decision tree's gain. The decision tree's entropy function is offered as a subset, allowing index gain to be calculated for various cases. The amount of information present in each computer node must be verified during the index gain process, and in this case, the total amount of information spread over the entire network is seen. All impurities are eliminated from the system once the gain has been maximised, and only pure data is sent to cloud computing systems. The entire degree of computing nodes is discernible during this removal process, which is simulated and depicted in Fig.  6 .

Comparison of gain values

The maximizing of gain for cloud computing that is created via a decision tree approach is shown in Fig.  6 and Table 5 . For experimental verification, between 1000 and 40,000 mobile nodes are taken into consideration, and the entropy values for each mobile node are calculated. Each mobile node has an entropy value that ranges from 230 to 635 and is defined by index ranges. Additionally, the index ranges are examined using various probability values; thus, the percentage of gain is calculated. It is seen during the comparison case that the proposed method, which uses a decision tree, provides a high gain in comparison to the existing cases. This can be demonstrated using 10,000 mobile nodes with an entropy of 425, where the projected method's gain is 93% and the existing method's gain is 81%. The projected approach with a decision tree offers excellent gain values even for mobile nodes that are constantly increasing, and a gain of about 98% is attained for networks with many computing nodes.

Performance evaluation

The best optimization for real-time cloud computing applications is chosen based on the performance evaluation that is simulated in this section. The smooth operation of mobile cloud computing depends on the integration of numerous distinct algorithms, so it is crucial to have an optimization tool that works well with an analytical working model. It is therefore preferable to distinguish between the status of transmitting and receiving devices, so a comparative case study is carried out using the best iteration cases. All parametric values must also be able to be evaluated by the integrated algorithm with proof-of-concept determination. Thus, the following evaluation is done for two key aspects of cloud computing:

Case study 1: Conjunction characteristics

Case study 2: Robustness characteristics

Case study 1: conjunction characteristics

High efficiency is attained if the data that is transmitted and received by the transmitting and receiving devices are converging quickly. If a decision tree is used in a machine learning system, the aforementioned scenario is possible. The fact that each subset's values are independently kept and the complete set is combined during the final loop is a key factor in decision trees' ease of convergence. Therefore, when the values of individual computing cases are stored inside the subset, individual cloud computing convergence increases. It is not very simple to ascertain how a computing process works, thus, index values are picked that provide suitable optimized values. A separate factor is used to remove impure data in order to achieve early convergence. Figure  7 simulates and depicts the convergence characteristics with the best iteration values.

Early convergence representation

It is evident from Fig.  7 and Table 6 that the decision tree's convergence point occurs significantly more quickly than it does for previous models. The decision tree converges at an early index point that starts from 60 while optimization in the existing model [ 5 ] fails to converge at an earlier rate, so 80 iteration periods are provided as the main index converging point. This can be shown using the five best epochs in step sizes of 20, 20, 40, 60, 80, and 100. Because every node in a decision tree converges at an early stage, it is significantly simpler to arrive to predicted solutions overall.

Case study 2: robustness characteristics

The strength of cloud computing nodes is studied in this case study, as well as the tolerance limit under all environmental circumstances. In general, computing nodes have substantially higher installation strengths than they do once the data has been sent. However, with mobile cloud computing, when more data branches are saved in a subset and only specific strengths are assessed, the aforementioned issue can be avoided if decision tree optimization is implemented. As a result, the process' overall efficiency increases to some amount while maintaining the functional aspects of cloud computing. The effectiveness of the suggested and existing methods is shown in Fig.  8 .

Flexibility determinations

It is clear from Fig.  8 and Table 7 that the strength of the computing process utilizing a decision tree is significantly larger than the example [ 5 ] at hand. Similar epoch values from a prior case study are picked in varied step sizes of 20, in order to validate this property. As data size varies throughout this process, a random variation is seen. Hence, the strength of cloud computing will be lower if less data size is provided. This can be shown using the best epoch values of 60 and 80, where the new method's equivalent robustness is 96 and 93 and the conventional method's is, respectively, 77 and 73.

An efficient simulation model is used to demonstrate how mobile cloud computing might be used to handle various emergency situations. Utilizing energy development variables that are operated with various energy kinds, a new mathematical model is created for both operation and analysis. The analytical representations are used to resolve the SNR values of mobile cloud computing, which are offered as a disadvantage in emergency situations. Using a loop generation technique, the activation function with gain and assigned bandwidth requirements are studied. For selecting the best networked cloud computing nodes, created loops are introduced directly in the suggested method with the aid of the MATLAB toolbox. A theoretical framework and a limited number of implementation categories were created in the analyzed literature, leaving out any mention of steady state mobile cloud computing systems. In order to overcome the aforementioned issue, the projected technique employs a decision tree optimization algorithm with a number of subset features. Since a decision tree method is included, indulgent network information totally removes the impure cloud computing nodes from the network. Additionally, the decision tree's index terms are constructed using entropy values, as a result, the computing model only works when it gets zero decibel points, which shows that all noise has been eliminated from the system. Five different situations are used to test the analytical framework, and comparisons with other approaches are also performed. The comparison case's results demonstrate that the proposed method which uses a decision tree, performs better than the standard procedure. Future applications of the suggested analytical framework for large-scale data processing networks can be made without the need for manual representation adjustments, and the same features can be integrated with extremely efficient optimization algorithms.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.61862051), UTM Research Fellow (No. 00P27), the Science and Technology Foundation of Guizhou Province (No.ZK[2022]549, No.[2019]1299), the Top-notch Talent Program of of Guizhou province (No.KY[2018]080), the Natural Science Foundation of Education of Guizhou province(No.[2019]203) and the Funds of Qiannan Normal University for Nationalities (No. qnsy2018003, No. qnsy2019rc09, No. qnsy2018JS013, No. qnsyrc201715).

The project was supported by the This work was supported by the National Natural Science Foundation of China (No.61862051), the Science and Technology Foundation of Guizhou Province (No.ZK[2022]549, No.[2019]1299), the Top-notch Talent Program of of Guizhou province (No.KY[2018]080), UTM Research Fellow (No. 00P27).

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Tao Hai & Jincheng Zhou

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Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, Duyun, 558000, China

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School of Computer &Communication, Lanzhou University of Technology, Lanzhou, 730050, China

School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun, 558000, China

Department of Computer Science, Kebri Dehar University, Kebri Dehar, Ethiopia

Shitharth Selvarajan

Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, 600123, Chennai, Tamil Nadu, India

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Conceptualization by Tao Hai, Dan Wang; Methodology by Dayang Jawawi and Hariprasath Manoharan; Software by Ye Lu and Jincheng Zhou; formal analysis by Dawang Jawani and Ebuka Ibeke investigation by Tao Hai and Dan Wang; Resources and data collection by Jincheng Zhou, Shitharth Selvarajan; Writing by: Hariprasath Manoharan, Dan Wang, Ye Lu and Tao Hai; Validation by: Ebuka Ibeke and Jincheng Zhou; Funding Acquisition by Shitharth Selvarajan. The author(s) read and approved the final manuscript.

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Hai, T., Zhou, J., Lu, Y. et al. An archetypal determination of mobile cloud computing for emergency applications using decision tree algorithm. J Cloud Comp 12 , 73 (2023). https://doi.org/10.1186/s13677-023-00449-z

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What is Cloud Computing?

Cloud computing is a technology that allows remote access to computing resources such as servers, storage, databases, networks, software, and analytics via the Internet. Instead of relying on local servers or personal devices to run applications, organizations and individuals can use a remote "cloud" of " servers to store and process data." This system is flexible and cost-effective, allowing users to pay for the resources they use.

Alright, so, you know how we all use apps, store photos, and run software on our devices? Well, cloud computing is like the behind-the-scenes magician making it all happen. Instead of relying on our own computers, it's like renting power from internet-connected supercomputers. These "cloud" servers handle everything – from storing your files to running complex applications. It's like having a virtual storage space and a powerhouse rolled into one. The cool part? You only pay for what you use. So, next time you save a document or binge-watch a show, remember you're tapping into the magic of cloud computing!

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Benefits of adopting cloud computing for businesses

Businesses can gain a great deal from adopting cloud computing, which can completely change how they function and plan in the digital world.

Cost Efficiency

• Businesses experience less financial burden since cloud computing eliminates the requirement for significant upfront hardware investments.
• Example: Let's say a startup releases a brand-new app. Rather than spending a lot of money on servers, they use cloud services. They only pay for the storage and processing power that they really use, which frees up funds for marketing and development.

Scalability

• Companies may readily adjust their resource levels in response to demand. This adaptability to transferring enterprise needs offers peak overall performance without requiring enormous infrastructure investments.
• Example: Imagine an e-commerce website during a holiday sale. Because of cloud scalability, users can shop with confidence as the site adapts automatically to growing traffic. Resources are reduced after the sale to save money.

Remote Cooperation

• Cloud services facilitate seamless communication across teams regardless of physical locations by enabling remote access to data and applications.
• Example: A design team works together on a project in a worldwide business setting. They may collaborate on the same files at the same time, no matter where they are, thanks to cloud tools.

Security Procedures

• Strong security measures like access controls, authentication, and encryption are frequently provided by cloud providers. Strengthening defenses against potential cyber threats is facilitated by automatic upgrades and disaster recovery capabilities.
• Example: An organization that handles finances shifts its operations to the cloud. The cloud provider’s advanced security features, such as encryption and multifactor authentication, protect sensitive customer data and assure compliance with industry standards.

Innovation and Efficiency

• Adoption of cloud computing propels organizations to the vanguard of innovation with the aid of presenting a dynamic and adaptable digital infrastructure. Consequently, quicker service and app deployment ends in expanded operational efficiency.
• Example: To run simulations, a research team needs a lot of processing power. They can swiftly access and launch virtual computers thanks to cloud computing, which speeds up their research and expands the realm of what is practical for them.

If you want to advance your career in technology, enroll in Cloud Computing training courses can provide the necessary skills and knowledge to rapid growly.

Cloud Computing Case Studies

Let’s dive into some of the popular case studies on cloud computing to decode how it has been a great asset in the current technological world.

Siemens Case Study

Let's look into the cloud computing case study of Siemens.

Background:

• Siemens, a global technology and engineering company, operates in various sectors, including energy.
• The energy sector faces challenges with numerous alerts and alarms in power plants, leading to increased operational complexity.
• High volume of alerts resulted in alert fatigue and reduced efficiency.
• Difficulty in distinguishing critical alerts from less urgent ones, impacting the ability to respond promptly to issues.

Solution: Siemens partnered with Amazon Web Services (AWS) to implement a cloud-based solution for optimizing alert management.

Implementation: 

• Leveraged AWS Cloud services to build a scalable and intelligent alerting system.
• Utilized AWS Lambda for serverless computing, enabling real-time processing of data.

Results: 

• Reduced power plant alerts by an impressive 90%, minimizing operational noise.
• Improved the ability to focus on critical alerts, enhancing overall plant efficiency.
• Achieved cost savings by leveraging the pay-as-you-go model of AWS services.

Technological Impact:

• Implemented machine learning algorithms to analyze historical data and predict potential issues, enabling proactive maintenance. I
• Integrated AWS CloudWatch for monitoring and AWS Simple Notification Service (SNS) for effective alert notifications.
• Operational Efficiency:
• Streamlined the monitoring process, allowing operators to respond swiftly to critical events. Enhanced decision-making by providing actionable insights derived from real-time data analysis.
• Scalability and Flexibility:
• AWS's scalable infrastructure ensured the system could handle increasing data volumes as the power plants expanded.
• Flexibility in deploying additional AWS services facilitated ongoing optimization and innovation.

User Experience: Improved overall user experience for plant operators by reducing cognitive load and allowing them to focus on critical tasks.

Future Prospects: Siemens continues to explore AWS services for further optimization, demonstrating a commitment to ongoing innovation and efficiency gains in power plant operations.

Dream 11 Case Study

Let's look into cloud computing case study of Dream11.

Background:  Dream11, India's largest fantasy sports platform, constantly seeks to enhance its technology infrastructure to provide users with a seamless and high-performance experience. Facing the challenge of optimizing costs while improving search functionality, Dream11 turned to Amazon OpenSearch Service for a strategic solution.

Challenges :

• Performance Enhancement: Dream11 aimed to boost the performance of its platform's search functionality, ensuring faster and more accurate results for users.
• Cost Optimization: Simultaneously, the company sought to optimize costs associated with the search infrastructure, aligning with efficient resource utilization.
• Integration of Amazon OpenSearch Service: Dream11 strategically chose Amazon OpenSearch Service to address its performance and cost optimization goals. The fully managed, open-source search and analytics service offered by AWS became a key component in upgrading Dream11's search functionality.

Key Achievements: 

• Performance Boost: Amazon OpenSearch Service enabled Dream11 to achieve a remarkable 40% improvement in the performance of its search functionality. Users experienced faster and more responsive search results, enhancing their overall experience on the platform.
• Cost Optimization: Leveraging the managed service model of Amazon OpenSearch, Dream11 successfully optimized costs associated with maintaining and scaling its search infrastructure. The platform could now efficiently allocate resources based on actual usage patterns.

Operational Efficiency: 

• Managed Service Model: Dream11 benefited from the fully managed nature of Amazon OpenSearch Service, reducing the operational overhead of maintaining and monitoring the search infrastructure.
• Scalability: The elastic nature of the service allowed Dream11 to scale its search capabilities dynamically, accommodating varying levels of user activity without compromising performance.

User Experience: 

• Faster and Accurate Results: With the enhanced performance of the search functionality, users enjoyed quicker and more accurate search results, contributing to an improved and satisfying user experience.
• Responsive Platform: Dream11's platform became more responsive, ensuring that users could swiftly find the information they were looking for, enhancing overall engagement.

Future Integration: 

• Continuous Optimization: Dream11 remains committed to continuous optimization and enhancement of its technology infrastructure. Future integration with AWS services and technologies could further improve various aspects of the platform.
• Innovation in Fantasy Sports Technology: The success of optimizing search functionality positions Dream11 to explore and implement innovative technologies in the realm of fantasy sports, offering users cutting-edge features and experiences.

BookMyShow Case Study

Let's look into the cloud computing case study on BookMyShow.

Background: BookMyShow, a prominent entertainment company in India, operates a comprehensive online ticketing platform and offers a range of services, including media streaming and event management.

Challenges: 

• Technical Debt: BookMyShow grappled with overprovisioned on-premises servers, resulting in unnecessary costs and inefficiencies.
• Scalability Concerns: The existing infrastructure struggled to dynamically scale according to fluctuating traffic volumes, leading to potential performance issues during peak times.

AWS Cloud Migration: 

• Strategic Partnership: BookMyShow collaborated with Amazon Web Services (AWS) and engaged Minfy Technologies, an AWS Premier Consulting Partner, to facilitate the migration of its data and applications to the AWS Cloud.
• Cost-Effective IT Architecture: The move to AWS aimed to create a more elastic and cost-effective IT infrastructure, aligning with BookMyShow's objectives for scalability and efficiency.
• Total Cost of Ownership (TCO) Improvement: BookMyShow achieved a significant 70 percent improvement in Total Cost of Ownership (TCO) by leveraging the cost-effective resources and scalability offered by AWS.
• Scalability: The AWS Cloud's elastic nature allowed BookMyShow to seamlessly scale its infrastructure in response to varying traffic demands, ensuring optimal performance during peak booking periods.
• Resource Optimization: By migrating to AWS, BookMyShow optimized resource allocation, eliminating the need for overprovisioned servers and reducing operational costs.
• Agility and Speed: The cloud environment provides agility and speed in deploying updates and features, contributing to a more responsive and efficient operational workflow.

Diverse Service Offerings: 

• Ticketing Platform: BookMyShow's online ticketing platform, which serves millions of customers across multiple regions, benefits from AWS's scalability and reliability.
• Media Streaming and Event Management: Beyond ticketing, AWS supports BookMyShow's diverse service offerings, including online media streaming and end-to-end event management for virtual and on-ground entertainment experiences.

Future Collaborations:

• Continuous Optimization: BookMyShow remains committed to continuous optimization, exploring further AWS services to enhance performance, security, and user experience.
• Innovation in Entertainment Technology: The collaboration with AWS positions BookMyShow to explore and implement innovative technologies, ensuring it stays at the forefront of the rapidly evolving entertainment tech landscape.

Source for Bookmyshow case study .

Pinterest Case Study

Let's look into the cloud computing case study on Pinterest.

Background: 

• Company: Pinterest, a visual discovery and bookmarking platform, relies on a robust and efficient built pipeline to ensure the quality and reliability of its iOS app.
• Objective: Enhancing the reliability of the iOS build pipeline to streamline the development process and deliver a seamless app experience.
• Build Pipeline Reliability: Pinterest faced challenges related to the reliability of its iOS build pipeline, impacting the speed and efficiency of app development.
• Resource Constraints: Traditional build infrastructure posed limitations, particularly for iOS development, where macOS environments are crucial.

Solution: 

• Amazon EC2 Mac Instances: Pinterest adopted Amazon EC2 Mac instances, leveraging macOS environments on the AWS cloud for iOS app builds.
• Scalability: The use of EC2 Mac instances allows Pinterest to scale resources dynamically based on the demand for iOS builds, optimizing performance and reducing bottlenecks.
• Reliability Improvement: By incorporating Amazon EC2 Mac instances, Pinterest achieved a remarkable 80.5% improvement in the reliability of its iOS build pipeline.
• Faster Development Cycle: The enhanced reliability translates to a more predictable and faster development cycle, enabling Pinterest to roll out app updates and features more efficiently.
• Parallel Build Processes: EC2 Mac instances enable Pinterest to run multiple iOS builds simultaneously in parallel, significantly reducing the overall build time.
• Cost Optimization: By utilizing EC2 Mac instances on a pay-as-you-go model, Pinterest optimizes costs, ensuring financial efficiency in infrastructure management.

Impact on Development Workflow: 

• Developer Productivity: The improved reliability and efficiency positively impact developer productivity, allowing them to focus on coding and innovation rather than troubleshooting build issues.
• Consistent Development Environment: EC2 Mac instances provide a consistent macOS development environment, minimizing compatibility issues and ensuring uniformity across the development lifecycle.
• Continuous Optimization: Pinterest continues to explore ways to optimize its build pipeline further, possibly incorporating additional AWS services or enhancements to the existing infrastructure.
• Broader Cloud Integration: The success of using EC2 Mac instances may encourage Pinterest to explore additional AWS cloud services for other aspects of its development and infrastructure needs.

Source for the Pinterest case study .

MakeMyTrip Case Study

Let's look into the cloud computing case study on MakeMyTrip.

Background:  MakeMyTrip, a leading online travel platform, caters to millions of users by providing a diverse range of travel services. In an ever-evolving and competitive industry, optimizing operational costs while maintaining robust performance is crucial. MakeMyTrip turned to Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS) to achieve this delicate balance.

• Cost Efficiency: MakeMyTrip aimed to reduce its compute costs without compromising the performance and reliability of its services.
• Scalability: With varying levels of user activity and traffic patterns, the platform required a solution that could scale dynamically to handle fluctuations in demand.
• Amazon ECS and EKS Integration: MakeMyTrip strategically chose Amazon ECS and EKS, Amazon's containerization solutions, to streamline its computing infrastructure.
• Containerization: Containerization technology allowed MakeMyTrip to encapsulate applications into isolated environments, optimizing resource utilization and ensuring consistent performance.
• 22% Cost Reduction: Leveraging Amazon ECS and EKS, MakeMyTrip achieved a noteworthy 22% reduction in compute costs. This cost optimization played a crucial role in enhancing the company's financial efficiency.
• Scalability: Amazon ECS and EKS's scalability features allowed MakeMyTrip to dynamically adjust its compute resources, ensuring optimal performance during peak travel booking periods.
• Resource Optimization: Containerization through ECS and EKS enabled MakeMyTrip to efficiently allocate and manage resources, reducing wastage and improving overall operational efficiency.
• Simplified Management: The container orchestration provided by ECS and EKS simplified the management of MakeMyTrip's applications, allowing for easier deployment and updates.

Scalability and Performance: 

• Dynamic Scaling: With ECS and EKS, MakeMyTrip could scale its applications seamlessly in response to changes in user demand, ensuring consistent and reliable performance.
• High Availability: The solutions' features for load balancing and automatic scaling contributed to high availability, minimizing downtime during peak travel seasons.
• Continuous Optimization: MakeMyTrip remains committed to continuous optimization, exploring additional AWS services and advancements in containerization technologies for further enhancements.
• Innovation in Travel Technology: The success of cost reduction and performance improvement positions MakeMyTrip to explore and implement innovative technologies, offering users an even more advanced and seamless travel experience.

Source for MakeupTrip case study .

McDonald’s Case Study

Let's look into the cloud computing case study on McDonald's.

Background:  McDonald's Corporation, a global fast-food giant, has embraced digital transformation to redefine its operations and enhance customer experiences. Utilizing the capabilities of Amazon Web Services (AWS), McDonald's has evolved into a digital technology company, achieving remarkable performance milestones in the process.

• Digital Transformation: McDonald's aimed to transition into a digital-first organization, leveraging technology to improve efficiency and customer interactions.
• Performance Targets: The company set ambitious performance targets, seeking to enhance its point-of-sale (POS) system to handle a massive volume of transactions seamlessly.
• AWS Cloud Integration: McDonald's strategically integrated with Amazon Web Services, utilizing its cloud infrastructure for scalable and efficient digital transformation.
• Cloud-Enabled Technology: AWS empowered McDonald's to implement cloud-enabled technologies, enabling a comprehensive overhaul of its systems and processes.
• Performance Milestones: McDonald's exceeded performance targets by up to 66%, showcasing the efficacy of its cloud-enabled digital transformation on AWS.
• Transactions Per Second: The POS system achieved an impressive capability to complete 8,600 transactions per second, demonstrating the scalability and efficiency of the cloud-based solution.

Operational Excellence: 

• Efficient Transactions: AWS provided the necessary infrastructure for McDonald's to conduct transactions with unprecedented efficiency, contributing to operational excellence.
• Scalability: The cloud-based solution ensured that McDonald's could scale its operations dynamically, accommodating fluctuations in customer demand seamlessly.

Customer Experience: 

• Enhanced Interactions: McDonald's digital transformation on AWS led to improved customer interactions, offering a more streamlined and responsive experience at the point of sale.
• Digital Services: Leveraging AWS, McDonald's expanded its digital services, catering to the evolving preferences of its tech-savvy customer base.

Real-Time Performance: 

• Dynamic Transactions: McDonald's achieved real-time processing capabilities, handling a substantial volume of transactions seamlessly through its POS system.

Future Prospects: 

• Continuous Innovation: McDonald's remains committed to continuous innovation, exploring new AWS services and technologies for further enhancements in its digital offerings.
• Global Expansion: The scalability and reliability of AWS position McDonald's for global expansion, ensuring a consistent and efficient digital experience across diverse markets.

Source for McDonald's case study .

Airbnb Case Study

Let's look into the cloud computing case study on Airbnb.

Background: Airbnb, a global online marketplace for lodging and travel experiences, faced the challenge of scaling its Continuous Integration/Continuous Deployment (CI/CD) pipeline to keep pace with the rapid expansion of its online marketplace. To address this, Airbnb turned to Amazon Elastic File System (EFS) and Amazon Simple Queue Service (SQS), leveraging AWS's scalable solutions.

• Scaling Infrastructure: As Airbnb experienced significant growth, the existing source control infrastructure needed to scale to meet the demands of an expanding online marketplace.
• Engineered Solution: To accommodate this growth, Airbnb sought a scalable and robust engineering solution for its CI/CD pipeline.
• Amazon EFS and SQS Integration: Airbnb strategically integrated Amazon EFS and Amazon SQS into its infrastructure, ensuring a scalable and efficient CI/CD pipeline.
• Scalable File Storage: Amazon EFS provided a scalable file storage solution, enabling Airbnb to handle increased data and file storage demands.
• Queue System: Amazon SQS was utilized to create a queue system, facilitating seamless communication and coordination within the CI/CD pipeline.
• Elimination of Scaling Concerns: With Amazon EFS and SQS in place, Airbnb overcame concerns about scaling its source control infrastructure, ensuring the ability to match the platform's exponential growth.
• Confidence in Scalability: The implementation instilled confidence in Airbnb's ability to scale its CI/CD pipeline in alignment with the expanding online marketplace.
• Efficient Source Control: Amazon EFS's scalable file storage system enhanced the efficiency of Airbnb's source control infrastructure, supporting a smooth CI/CD pipeline operation.
• Seamless Communication: Amazon SQS's queue system ensured seamless communication between different components of the CI/CD pipeline, minimizing bottlenecks.

Real-Time Impact: 

• Responsive Growth: The integration of Amazon EFS and SQS allowed Airbnb's CI/CD pipeline to respond dynamically to the platform's growth, ensuring a responsive and efficient development workflow.

Future Scalability: 

• Continuous Improvement: Airbnb remains committed to continuous improvement, exploring additional AWS services and technologies to further enhance the scalability and efficiency of its CI/CD pipeline.
• Scalability Assurance: The successful implementation of Amazon EFS and SQS assures Airbnb that it can confidently scale its infrastructure to meet future growth challenges.

Source for Airbnb case study .

Yulu Case Study

Let's look into the cloud computing case study of Yulu.

Background:  Yulu, a prominent micro-mobility service provider, sought to enhance its service efficiency by leveraging predictive analytics. Through the implementation of a robust prediction model and the utilization of Amazon Web Services (AWS) data lake capabilities, Yulu aimed to optimize its operations and deliver an improved experience to its users.

• Service Efficiency: Yulu faced challenges related to optimizing service efficiency, including fleet management, resource allocation, and user experience.
• Data Utilization: Leveraging the wealth of data generated by its micro-mobility services, Yulu aimed to extract actionable insights to drive operational improvements.
• Prediction Model Implementation: Yulu deployed a sophisticated prediction model to analyze historical and real-time data, forecasting demand, and optimizing resource allocation.
• AWS Data Lake Integration: To effectively manage and analyze large volumes of data, Yulu utilized AWS data lake capabilities, providing a scalable and secure infrastructure.
• Service Efficiency Improvement: The implementation of the prediction model and the utilization of AWS data lake resulted in a substantial improvement in service efficiency, with Yulu achieving a 30–35% enhancement.
• Optimized Resource Allocation: The prediction model enabled Yulu to allocate resources more effectively, ensuring that micro-mobility assets were positioned strategically based on anticipated demand.

Operational Excellence:

• Real-time Data Analysis: The prediction model, coupled with AWS data lake capabilities, allowed Yulu to perform real-time analysis of data, enabling swift and informed decision-making.
• Cost Optimization: Yulu optimized costs associated with fleet management and resource allocation, aligning expenses with actual demand patterns.
• Enhanced Availability: With improved service efficiency, Yulu enhanced the availability of its micro-mobility services, providing users with a more reliable and accessible transportation option.
• Predictive Features: Users benefited from predictive features, such as accurate arrival times and availability forecasts, contributing to an overall enhanced experience.

Future Optimizations: 

• Continuous Model Refinement: Yulu is committed to continuous refinement of its prediction model, incorporating new data and feedback to further enhance service efficiency.
• Expanded Data Utilization: The success of AWS data lake integration encourages Yulu to explore additional ways to leverage data for innovation and business optimization.

Source for Yulu bike case study .

Canva Case study

Let's look into the cloud computing case study of Canva.

Background: Canva, a leading graphic design platform, faced the dual challenge of scaling to accommodate its rapidly growing user base, reaching 160 million monthly active users while concurrently managing and optimizing costs. To address this challenge, Canva strategically leveraged the breadth of Amazon Elastic Compute Cloud (EC2) purchase models and cost optimization tools offered by AWS.

• Scalability: With a massive user base, Canva needed to scale its infrastructure to handle increasing user demands seamlessly.
• Cost Management: As the user base expanded, cost management became crucial. Canva aimed to optimize costs without compromising on performance.
• Amazon EC2 Purchase Models: Canva utilized a mix of Amazon EC2 purchase models, including On-Demand Instances, Reserved Instances, and Spot Instances, to match its diverse workload requirements with cost-effective options.
• Cost Optimization Tools: Leveraging AWS's suite of cost optimization tools, Canva implemented strategies to identify and eliminate inefficiencies, ensuring optimal resource utilization.
• Scale to 160 million Users: Canva successfully scaled its infrastructure to accommodate 160 million monthly active users, meeting the demands of a rapidly growing user base.
• Cost Control: The strategic use of Amazon EC2 purchase models and cost optimization tools allowed Canva to effectively control costs, aligning expenses with actual workload needs.
• Workload Matching: The flexibility of Amazon EC2 purchase models enabled Canva to match diverse workloads with the most cost-effective instance types, optimizing resource utilization.
• Efficient Resource Allocation: AWS cost optimization tools identified and rectified inefficiencies, ensuring efficient resource allocation and reducing unnecessary expenses.
• Scalable Performance: Canva's scalable infrastructure supported a seamless and responsive user experience, even with the significant increase in monthly active users.
• Consistent Service Availability: The optimization efforts contributed to consistent service availability, enhancing reliability for Canva's global user base.
• Dynamic Workload Management: The adaptability of EC2 purchase models allowed Canva to dynamically manage its workload, adjusting resources based on real-time demands.
• Cost Visibility: The implementation of AWS cost optimization tools provided real-time visibility into expenses, allowing Canva to make informed decisions to control costs.

Future Strategies: 

• Continuous Optimization: Canva remains committed to continuous optimization, exploring new EC2 purchase models and cost optimization tools to further refine its infrastructure.
• Innovation and Growth: The successful management of costs positions Canva for continued innovation and growth, ensuring that the platform can evolve to meet the needs of its expanding user base.

Source for Canva case study .

McAfee Case study

Let's look into the cloud computing case study of McAfee.

Background: McAfee, a global leader in the cybersecurity industry, aimed to significantly enhance the performance and efficiency of its operations, particularly in managing a colossal volume of daily transactions. To achieve this, McAfee turned to Amazon Elastic Block Store (EBS), specifically leveraging the high-performance capabilities of Amazon EBS io2 Block Express volumes.

• Performance Optimization: McAfee faced challenges in optimizing its operations' performance, especially concerning the management of many daily transactions.
• Backup Time: Efficient backup processes were crucial, and McAfee sought ways to streamline and expedite its backup procedures.
• Amazon EBS Integration: McAfee strategically integrated Amazon EBS into its infrastructure, harnessing the capabilities of Amazon EBS io2 Block Express volumes for enhanced performance.
• High-Performance Storage: The adoption of io2 Block Express volumes allowed McAfee to leverage high-performance storage, crucial for managing the demanding workload of daily transactions.
• Performance Enhancement: McAfee achieved a substantial 30% improvement in overall performance, optimizing its ability to handle and process 400 million daily transactions.
• Backup Time Reduction: The integration of Amazon EBS io2 Block Express volumes resulted in a significant 50% reduction in backup time, streamlining critical backup processes.
• Efficient Data Management: Amazon EBS provided McAfee with efficient data management capabilities, ensuring that the cybersecurity company could handle daily transactions seamlessly.
• Reliable Storage: The high-performance storage offered by io2 Block Express volumes contributed to the reliability and responsiveness of McAfee's operations.

Cost Efficiency: 

• Optimized Resource Utilization: McAfee optimized resource utilization with Amazon EBS, ensuring that storage resources were allocated efficiently to meet performance demands.
• Cost-Effective Scalability: The scalable nature of EBS io2 Block Express volumes allowed McAfee to align costs with actual storage and performance requirements.

Future Optimization: 

• Continuous Performance Tuning: McAfee remains committed to continuous performance tuning, exploring additional AWS services and advancements to further optimize its operations.
• Exploring Innovations: The success with Amazon EBS opens the door for McAfee to explore further innovations and integrations within the AWS ecosystem.

Source for McAfee case study .

You might have noticed some of the top companies using Amazon Web Services to deploy their application. You can also become an AWS Certified solution architect by enrolling in Cloud Computing course .

In conclusion, the adoption of cloud computing offers unparalleled benefits for businesses in the modern digital landscape. Cloud computing provides a flexible and scalable infrastructure, allowing organizations to efficiently manage resources based on demand. The cost-effectiveness of cloud services, eliminating the need for extensive upfront investments in hardware and maintenance, empowers businesses of all sizes.

With the ability to leverage advanced technologies, rapid innovation, and global reach, cloud computing emerges as a catalyst for sustainable growth, agility, and resilience in today's dynamic business environment. As businesses navigate the future, embracing cloud computing remains pivotal for staying competitive, adaptive, and prepared for the ever-evolving landscape of the digital economy.

Frequently Asked Questions

Cloud computing facilitates secure storage and sharing of patient records, enabling seamless collaboration among healthcare professionals. 

Financial institutions leverage the cloud for data analysis, risk management, and customer-facing applications, ensuring real-time insights and enhanced customer experiences.

Cloud allows businesses to scale resources up or down based on demand, ensuring optimal performance and cost efficiency. Cloud services provide flexibility by enabling remote access to data and applications, fostering collaboration and adaptability in a dynamic business environment.

Businesses should prioritize providers with robust security protocols to safeguard sensitive data. The chosen provider should offer scalable solutions to accommodate business growth and evolving needs effectively.

Businesses may face challenges in ensuring data security and compliance during the migration process. Compatibility and integration with existing systems can pose challenges, impacting the seamless transition to the cloud.

Kingson Jebaraj

Kingson Jebaraj is a highly respected technology professional, recognized as both a Microsoft Most Valuable Professional (MVP) and an Alibaba Most Valuable Professional. With a wealth of experience in cloud computing, Kingson has collaborated with renowned companies like Microsoft, Reliance Telco, Novartis, Pacific Controls UAE, Alibaba Cloud, and G42 UAE. He specializes in architecting innovative solutions using emerging technologies, including cloud and edge computing, digital transformation, IoT, and programming languages like C, C++, Python, and NLP. 

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