mobile cloud computing case study

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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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Internet is growing in many ways, be it the importance, functionalities, growth, users and last but not the least, the ease with which it can be accessed. Way back the access was via limited dialup connections at home, or the company network in the offices. Later its use was made easy with home broadband connections, Internet cafes, Wi-Fi based wireless hotspots and now the access is as simple as a click or simple touch, appreciation to smartphones, iPhones and other smart mobile devices that have made the use as easy and essential as breathing in. Not to forget the advent of Cloud computing that has become as an important paradigm to overcome problems faced by organizations to use expensive software and other services. It is one of the most important archetype shifts of the past decades. The approach promotes deployment of services with security, good performance and maintenance and various other remarkable features. This paper highlights computing technologies that are so much eng...

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Cloud computing is an on demand service in which distributed resources, information, software and other devices are provided according to the client's requirement at specific time 1 . Cloud computing involves deploying groups of remote servers and software networks that allow centralized data storage and online access to computer services or resources. In this paper, we explore the different services in different computing platforms and applications. Cloud computing is a service, which offers customers to work over the internet 2 . Kyi Pyar | Me Me Khaing "Cloud Computing Basics: Features and Services" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27960.pdf

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

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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

Mobile cloud computing for indoor emergency response: the IPSOS assistant case study

• Original Article
• Published: 22 July 2019
• Volume 5 , pages 173–191, ( 2019 )

Cite this article

• Dario Facchinetti   ORCID: orcid.org/0000-0001-7534-6055 1 ,
• Giuseppe Psaila   ORCID: orcid.org/0000-0002-9228-560X 1 &
• Patrizia Scandurra   ORCID: orcid.org/0000-0002-9209-3624 1  

279 Accesses

11 Citations

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Mobile Cloud Computing and Internet of Things provide pervasive connected infrastructures that allow people to be continuously connected with other people, services, and objects. However, technologies for multi-paradigm distributed computing are still in their infancy and lack of standardization and experimentation as pervasive well-being technology . In this article, we discuss about the use of mobile cloud computing technologies to devise a new generation of software systems for Indoor Emergency Response with better automation, flexibility, efficiency, and rich user experience. To this end, we present a prototype cloud-enabled mobile app, named IPSOS Assistant , for monitoring people well-being and managing emergencies in indoor environments. Such a prototype application is aimed at increasing the reliability and safety in indoor workplaces to prepare for and manage a variety of emergencies or incidents by giving people assistance (e.g., showing available escape routes in case of fire) and by taking advantage from the social contribution of other people located nearby in the same building. The IPSOS Assistant app has been designed and developed since 2014 as an academic/research pilot project. In this article, we share the design, discuss faced challenges, and report our experiments and lessons that we learned while developing such a kind of application.

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Internet of Things: Challenges and Opportunities

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Acknowledgements

We warmly thank the IPSOS student group : Francesco Biffi, Enrico Mazzucchelli, Andrea Rota, Steven Rovelli and Matteo Taiocchi.

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Dario Facchinetti, Giuseppe Psaila & Patrizia Scandurra

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Facchinetti, D., Psaila, G. & Scandurra, P. Mobile cloud computing for indoor emergency response: the IPSOS assistant case study. J Reliable Intell Environ 5 , 173–191 (2019). https://doi.org/10.1007/s40860-019-00088-9

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Received : 04 February 2019

Accepted : 13 July 2019

Published : 22 July 2019

Issue Date : 01 September 2019

DOI : https://doi.org/10.1007/s40860-019-00088-9

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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.”

Mobile Cloud Computing Explained

Introduction.

Mobile Cloud Computing (MCC) is an architectural approach that combines the processing power of mobile devices like smartphones or tablets with cloud-based resources. Remotely rather than locally, as a result of computational augmentations, MCC’s mobile devices may augment resources from various cloud-based accounts. The combination creates a new kind of mobile computing that provides a seamless experience on any device, or when switching between devices.

Mobile Cloud Computing Architecture

The Mobile Cloud Computing Architecture Consists of Two Major Components.

How One AI-Driven Media Platform Cut EBS Costs for AWS ASGs by 48%

The first major component is the virtualized computing core (VC), a hosted cloud service that hosts various cloud computing services needed to run on the mobile device.

The second major component is the client-side application (CSA): It executes the MCC applications on the host device. The CSA uses a cloud execution service when executing applications for a client. During the execution of the MCC application in the CES, it can use various cloud resources to augment its capabilities.

Types Of Mobile Cloud Computing

Types of Cloud-Based Resources in MCC Are:

• Distant Immobile Cloud Computing
• Hybrid Cloud Computing
• Distant Mobile Clouds
• Proximate Immobile Computing Entities
• Proximate Mobile Computing Entities

Amazon EC2 is an example of a distant immobile class. Cloudlets or surrogates are movable computing entities that are proximate immobile. Smartphones, tablets, and handheld gadgets falls to the class of mobile computing entities. Click here for more info.

Applications And Examples of Mobile Cloud Computing.

A mobile cloud program is a software program that we can access via our onboard computer. There are several real-life examples of cloud solutions, such as:

Email : This is a prominent example that lots of people use. Gmail, Outlook, and Yahoo Mail are numerous examples of mobile email. When you check your emails through your smartphone, you’re using mobile cloud computing technology.

Social Media : It enables quick sharing of real-time data on social media platforms like Twitter, Instagram, and Facebook. For example, a video recorded on a mobile device can be saved and shared with another mobile user.

Finance and Commerce:  Using your phone or tablet to track your account balance, making a purchase on ecommerce platforms such as Amazon, Shopify, etc., is an example of mobile cloud computing, and its scalability makes it ideal for commerce and social media as well.

Healthcare : With cloud computing, accessing patient records through a mobile device is simple. Mobile healthcare also permits massive amounts of instantaneous data stored in the cloud, accessible via a mobile device. It enables convenience by allowing access to patient records when needed.

Why Mobile Cloud Computing?

Mobile Cloud Computing allows faster execution of applications because it has a built-in web browser used to execute the application. Applications can be executed even when a desktop or server-based applications are not available. It’s easy to use and applies a wide variety of one-handed devices at once.

Easier development and deployment:  Mobile cloud computing provides an easy method of creating an application with minimum development efforts. It has more resource-efficient than traditional desktops and server-based software applications. It helps reduce capital expenses, also called CapEx, making mobile cloud computing a more cost-effective solution.

Increased Uptime : mobile cloud computing can provide higher uptime than traditional applications and is thus superior to those. You don’t have to invest in responsive machinery or servers that will operate for only a limited amount of time, both of which must be switched off after a certain period. Mobile cloud computing utilizes virtualized technology that allows it when and where needed, thereby increasing reliability. Mobile cloud applications have a higher degree of accessibility than traditional software.

Service Models of Mobile Cloud Computing

In mobile cloud computing, there are three service models. Software as a Service ( SaaS ) is an approach to delivering software to the user via the internet on a subscription without installing anything. In Infrastructure as a Service ( IaaS ), computers and other computational resources are transferred from one organization to another. It lets the company provide server capacity, storage, and more for customers who don’t have these things in-house. It also lets them manage these resources with their staff of IT managers.

The last model is Platform as a Service ( PaaS ). In this model, the provider manages the virtualization of an operating system and makes applications available to the user for them to install and run. Applications are often integrated with other business systems. For example, Salesforce.com includes software from SAP, Oracle, and Microsoft.

Among these three service models, cloud computing has emerged as an effective way to solve many computing problems such as storage, sharing of a large amount of data, and providing internet connection. Software as a Service (SaaS) and Platform as a Service (PaaS) are the two main service models in Cloud computing.

Advantages Of Mobile Cloud Computing

Mobile Cloud Computing provides more benefits for business clients than traditional desktop or server-based applications. Following are the benefits offered by Mobile Cloud Computing:

Cost-Effective  – Mobile cloud computing uses virtualized computing resources that the cloud providers can easily provide at much lower costs than hosting software on mobile devices. It saves the cost of maintenance and operation, and hardware as clients tend to consume lesser power resources than their physical counterparts.

Flexible  – Mobile cloud computing allows for flexibility in the usage of a device. Through usage in a cloud, the device can be easily and quickly shifted from one application to another. We can use the same device to run different applications as required by the user or the aim of execution.

Scalable  – Wireless cloud computing handles scalability automatically and without physical limitations by operating on cloud platforms.

Affordable  – Mobile cloud computing allows for the sole use of a device to host the applications and services while ensuring that they are available at a specific location. With this, you can access it anywhere and have full control over your devices.

Easy Updates  – Updates are also easy to provide as applications need only to be hosted in the cloud server. And before updating, the device did not need any recertification.

Faster execution  – Mobile cloud computing takes advantage of the capabilities of a single device or device group and utilizes its capabilities through web services. The selection of this application is faster due to the multitasking that occurs through spatial-visual processes. These benefits are more prominent for mobile devices with limited memory and processing power than desktop or server-based computers.

Though Cloud computing can be a solution to some of the issues with mobile cloud computing, it also has limitations. For instance, desktop applications cannot use cloud resources, and only device synchronization is limited to the latest changes. There is no infrastructure for distributed applications on the mobile cloud because there is no storage and maintaining virtual environments for individual programs on such a platform. Due to this reason, mobile cloud computing has yet to gain widespread acceptance among developers and requires certain improvements in the way it is composed and executed. Click here for more benefits.

Practices for Implementing Mobile Cloud Computing

Access Management Solution and Deploy Identity : Ensure solid access policies are in place to restrict accessibility and strengthen resources by enforcing least privilege rules. Privileged access should use session monitoring to audit and record access, ensuring privileges are role-based and minimal access necessary to operate is granted. With a zero-trust model, access will be tightly controlled, needing every person, electronic device, or system to be cross-checked and validated before connecting to your network’s systems or devices outside of the network perimeter.

Secure Your Endpoint : Coming up with new cloud computing projects isn’t always enough to ensure endpoint security is improved, as it’s indefinite. It can be beneficial to revisit existing security practices and confirm they are suitable for the new threats to get a start.

A standard defense-in-depth methodology incorporating firewalls, anti-malware, intrusion detection, and access control has been traditional for endpoint security. However, the impact of endpoint security considerations can be so complex that automation tools are necessary to stay abreast of the fashion. And in this case, Endpoint detection and response (EDR) tools and endpoint protection platforms (EPP) can help resolve this issue.

Vulnerability Management : Scan for vulnerabilities and misconfigurations and conduct security audits and testing to identify system vulnerabilities and possible security threats. Perform penetration testing of your network’s environment (on-premises and cloud) to determine risks and vulnerabilities.

Patch management : Create systematic processes to identify vulnerabilities in your system, and your cloud vendor’s security processes are updated regularly to crack down on known vulnerabilities. And also, you should analyze post-patching effects to address any issues between systems and environments.

Monitoring user activity : Analyze how cloud users use your business’s cloud environment. Evaluate your cloud users’ cloud culture as well. Casual use of data and data sharing could yield substantive personal data risks. For example, cloud computing enables users to retrieve automatically, archive, and transmit information from various sources. Soaring sharing could result not only in legitimate data leakages but additionally in data transmitted by non-authorized sources.

Password Management : Use these best practices for password management, including:

• Configure a minimum number of characters required for your password.
• Indicate the complexity requirements for passwords.
• Include a minimum of 10 previous passwords in the password history.
• Cancel existing passwords every ninety days, and set a security maximum password age and constant email alerts.
• To maintain a fresh set of local admin account passwords every month and yearly service account passwords, the administrator should reset them each 180 days.
• Tracking all password changes can aid with password auditing.
• Balance security concerns at different systems with an enterprise password management service to ensure consistent strong security at all levels.

Compliance Management : Compile alerts to select an audiovisual system so you can be notified when your organization may be out of compliance with applicable laws to avert untoward consequences.

Encryption : Your company’s enterprise data ought to be protected by data encryption in effect at all times. Consider the extra use of several encryption services throughout your databases, servers, and networks.

Monitoring : When choosing your cloud computing service and company, make sure they have continuous security monitoring for each environment and all systems.

Security Reporting : Review and adjust your cloud supplier’s platform-specific alerts and reports to locate and centralize your data from all connections and examine all environments at once to uncover a complete record of the computing environment’s security posture.

Challenges of Mobile Cloud Computing

Mobile Cloud Technology Faces Several Challenges, Such as The Issues Listed Below.

Security  – The security provided by traditional applications is not applicable for mobile cloud computing. Mobile cloud computing is highly dependent upon the security of its execution environment. Hence, it poses more security risks than traditional applications since many external factors have access to the data sent to and received from the mobile cloud. Mobile cloud computing requires additional investment in hardware and software to ensure additional security while maintaining its high-performance levels.

Network Availability:  The reliability of mobile networks influences the availability of mobile cloud computing services. If we lose network access entirely, we cannot use the application. This can be a limiting factor in areas with no high-speed internet access or limited network coverage.

Lack of Infrastructure  – There is a lack of infrastructure for distributed applications as there is no means for creating virtual devices for running such applications.

performance: Mobile cloud apps are accessed across public networks hosted by remote servers. This leads to slower response in mobile cloud application.

Compatibility : The cloud infrastructure supports multiple platforms, which may be expensive to implement due to the requirements of different network connections.

In conclusion, MCC is an innovative approach to mobile computing that provides users with expanded capabilities and greater flexibility. MCC enables users to take full advantage of the capacity of their smartphones or tablets by combining the processing power of these devices with that available in the cloud. This hybrid approach can provide users with more functionality than they have on their devices alone, as well as additional services not available on either device alone.

MCC relies on cloud computing and mobile devices to create an environment where users can access the functionality of both of their devices without the need for separate servers. Since MCC allows for greater user interaction with each device, this hybrid method can utilize resources from a single client without relying on multiple nodes in the server-client relationship.

By following cloud security best practices and implementing the appropriate security tools, businesses can minimize risks and take full advantage of the benefits cloud computing offers.

Cloud computing provides many advantages such as cost savings, increased flexibility, and decreased risk of downtime. These advantages, though, are only accessible if your architecture is planned properly. Before starting a new MCC project, especially with existing customers, it’s recommended to have a professional cloud architect on board. If you’re consulting a third-party technology partner, make sure it’s one with similar projects in their resume.

A scalable, cost-effective MCC architecture is now easier than ever to achieve, with GlobalDots. Our seasoned cloud architects plan & execute cloud infrastructure projects for some of the world’s largest airlines, banks, retailers, and online businesses. Contact us for a commitment-free discovery call.

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Cloud Computing Case Studies and Success Stories 2024

Home Blog Cloud Computing Cloud Computing Case Studies and Success Stories 2024

Have you ever wondered how all those services and apps operate so smoothly together to improve our digital lives? All of this is possible because of cloud computing, the unsung hero of the computer industry.

Imagine a business that is trying to innovate and improve its processes as it faces obstacles. Come into the cloud and intervene to rescue the day. Let me take you behind the scenes to witness the hardships, "Aha!" moments, and remarkable advantages this switch brought about.

Picture it as a beautiful performance where data and virtualization work together smoothly, creating a story that goes beyond just technology – it's a big change in how businesses work. Get ready for a journey into something amazing, where the cloud isn't just a fix; it's like the main character in a story of new and creative ways of doing things in the business world. In this article, I will take you across some of the cool cloud computing case study examples and highlight cloud implementation in those cases.

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!

You can explore Knowledgehut Cloud Computing training courses to learn more about cloud computing.

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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Upcoming Cloud Computing Batches & Dates

Published: 5 April 2024 Contributors: Tim Mucci, Cole Stryker

Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights. Big data analytics allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions. This process allows organizations to leverage the exponentially growing data generated from diverse sources, including internet-of-things (IoT) sensors, social media, financial transactions and smart devices to derive actionable intelligence through advanced analytic techniques.

In the early 2000s, advances in software and hardware capabilities made it possible for organizations to collect and handle large amounts of unstructured data. With this explosion of useful data, open-source communities developed big data frameworks to store and process this data. These frameworks are used for distributed storage and processing of large data sets across a network of computers. Along with additional tools and libraries, big data frameworks can be used for:

• Predictive modeling by incorporating artificial intelligence (AI) and statistical algorithms
• Statistical analysis for in-depth data exploration and to uncover hidden patterns
• What-if analysis to simulate different scenarios and explore potential outcomes
• Processing diverse data sets, including structured, semi-structured and unstructured data from various sources.

Four main data analysis methods  – descriptive, diagnostic, predictive and prescriptive  – are used to uncover insights and patterns within an organization's data. These methods facilitate a deeper understanding of market trends, customer preferences and other important business metrics.

IBM named a Leader in the 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions.

Structured vs unstructured data

What is data management?

The main difference between big data analytics and traditional data analytics is the type of data handled and the tools used to analyze it. Traditional analytics deals with structured data, typically stored in relational databases . This type of database helps ensure that data is well-organized and easy for a computer to understand. Traditional data analytics relies on statistical methods and tools like structured query language (SQL) for querying databases.

Big data analytics involves massive amounts of data in various formats, including structured, semi-structured and unstructured data. The complexity of this data requires more sophisticated analysis techniques. Big data analytics employs advanced techniques like machine learning and data mining to extract information from complex data sets. It often requires distributed processing systems like Hadoop to manage the sheer volume of data.

These are the four methods of data analysis at work within big data:

The "what happened" stage of data analysis. Here, the focus is on summarizing and describing past data to understand its basic characteristics.

The “why it happened” stage. By delving deep into the data, diagnostic analysis identifies the root patterns and trends observed in descriptive analytics.

The “what will happen” stage. It uses historical data, statistical modeling and machine learning to forecast trends.

Describes the “what to do” stage, which goes beyond prediction to provide recommendations for optimizing future actions based on insights derived from all previous.

The following dimensions highlight the core challenges and opportunities inherent in big data analytics.

The sheer volume of data generated today, from social media feeds, IoT devices, transaction records and more, presents a significant challenge. Traditional data storage and processing solutions are often inadequate to handle this scale efficiently. Big data technologies and cloud-based storage solutions enable organizations to store and manage these vast data sets cost-effectively, protecting valuable data from being discarded due to storage limitations.

Data is being produced at unprecedented speeds, from real-time social media updates to high-frequency stock trading records. The velocity at which data flows into organizations requires robust processing capabilities to capture, process and deliver accurate analysis in near real-time. Stream processing frameworks and in-memory data processing are designed to handle these rapid data streams and balance supply with demand.

Today's data comes in many formats, from structured to numeric data in traditional databases to unstructured text, video and images from diverse sources like social media and video surveillance. This variety demans flexible data management systems to handle and integrate disparate data types for comprehensive analysis. NoSQL databases , data lakes and schema -on-read technologies provide the necessary flexibility to accommodate the diverse nature of big data.

Data reliability and accuracy are critical, as decisions based on inaccurate or incomplete data can lead to negative outcomes. Veracity refers to the data's trustworthiness, encompassing data quality, noise and anomaly detection issues. Techniques and tools for data cleaning, validation and verification are integral to ensuring the integrity of big data, enabling organizations to make better decisions based on reliable information.

Big data analytics aims to extract actionable insights that offer tangible value. This involves turning vast data sets into meaningful information that can inform strategic decisions, uncover new opportunities and drive innovation. Advanced analytics, machine learning and AI are key to unlocking the value contained within big data, transforming raw data into strategic assets.

Data professionals, analysts, scientists and statisticians prepare and process data in a data lakehouse, which combines the performance of a data lakehouse with the flexibility of a data lake to clean data and ensure its quality. The process of turning raw data into valuable insights encompasses several key stages:

• Collect data: The first step involves gathering data, which can be a mix of structured and unstructured forms from myriad sources like cloud, mobile applications and IoT sensors. This step is where organizations adapt their data collection strategies and integrate data from varied sources into central repositories like a data lake, which can automatically assign metadata for better manageability and accessibility.
• Process data: After being collected, data must be systematically organized, extracted, transformed and then loaded into a storage system to ensure accurate analytical outcomes. Processing involves converting raw data into a format that is usable for analysis, which might involve aggregating data from different sources, converting data types or organizing data into structure formats. Given the exponential growth of available data, this stage can be challenging. Processing strategies may vary between batch processing, which handles large data volumes over extended periods and stream processing, which deals with smaller real-time data batches.
• Clean data: Regardless of size, data must be cleaned to ensure quality and relevance. Cleaning data involves formatting it correctly, removing duplicates and eliminating irrelevant entries. Clean data prevents the corruption of output and safeguard’s reliability and accuracy.
• Analyze data: Advanced analytics, such as data mining, predictive analytics, machine learning and deep learning, are employed to sift through the processed and cleaned data. These methods allow users to discover patterns, relationships and trends within the data, providing a solid foundation for informed decision-making.

Under the Analyze umbrella, there are potentially many technologies at work, including data mining, which is used to identify patterns and relationships within large data sets; predictive analytics, which forecasts future trends and opportunities; and deep learning , which mimics human learning patterns to uncover more abstract ideas.

Deep learning uses an artificial neural network with multiple layers to model complex patterns in data. Unlike traditional machine learning algorithms, deep learning learns from images, sound and text without manual help. For big data analytics, this powerful capability means the volume and complexity of data is not an issue.

Natural language processing (NLP) models allow machines to understand, interpret and generate human language. Within big data analytics, NLP extracts insights from massive unstructured text data generated across an organization and beyond.

Structured Data

Structured data refers to highly organized information that is easily searchable and typically stored in relational databases or spreadsheets. It adheres to a rigid schema, meaning each data element is clearly defined and accessible in a fixed field within a record or file. Examples of structured data include:

• Customer names and addresses in a customer relationship management (CRM) system
• Transactional data in financial records, such as sales figures and account balances
• Employee data in human resources databases, including job titles and salaries

Structured data's main advantage is its simplicity for entry, search and analysis, often using straightforward database queries like SQL. However, the rapidly expanding universe of big data means that structured data represents a relatively small portion of the total data available to organizations.

Unstructured Data

Unstructured data lacks a pre-defined data model, making it more difficult to collect, process and analyze. It comprises the majority of data generated today, and includes formats such as:

• Textual content from documents, emails and social media posts
• Multimedia content, including images, audio files and videos
• Data from IoT devices, which can include a mix of sensor data, log files and time-series data

The primary challenge with unstructured data is its complexity and lack of uniformity, requiring more sophisticated methods for indexing, searching and analyzing. NLP, machine learning and advanced analytics platforms are often employed to extract meaningful insights from unstructured data.

Semi-structured data

Semi-structured data occupies the middle ground between structured and unstructured data. While it does not reside in a relational database, it contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Examples include:

• JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) files, which are commonly used for web data interchange
• Email, where the data has a standardized format (e.g., headers, subject, body) but the content within each section is unstructured
• NoSQL databases, can store and manage semi-structured data more efficiently than traditional relational databases

Semi-structured data is more flexible than structured data but easier to analyze than unstructured data, providing a balance that is particularly useful in web applications and data integration tasks.

Ensuring data quality and integrity, integrating disparate data sources, protecting data privacy and security and finding the right talent to analyze and interpret data can present challenges to organizations looking to leverage their extensive data volumes. What follows are the benefits organizations can realize once they see success with big data analytics:

Real-time intelligence

One of the standout advantages of big data analytics is the capacity to provide real-time intelligence. Organizations can analyze vast amounts of data as it is generated from myriad sources and in various formats. Real-time insight allows businesses to make quick decisions, respond to market changes instantaneously and identify and act on opportunities as they arise.

Better-informed decisions

With big data analytics, organizations can uncover previously hidden trends, patterns and correlations. A deeper understanding equips leaders and decision-makers with the information needed to strategize effectively, enhancing business decision-making in supply chain management, e-commerce, operations and overall strategic direction.  

Cost savings

Big data analytics drives cost savings by identifying business process efficiencies and optimizations. Organizations can pinpoint wasteful expenditures by analyzing large datasets, streamlining operations and enhancing productivity. Moreover, predictive analytics can forecast future trends, allowing companies to allocate resources more efficiently and avoid costly missteps.

Better customer engagement

Understanding customer needs, behaviors and sentiments is crucial for successful engagement and big data analytics provides the tools to achieve this understanding. Companies gain insights into consumer preferences and tailor their marketing strategies by analyzing customer data.

Optimized risk management strategies

Big data analytics enhances an organization's ability to manage risk by providing the tools to identify, assess and address threats in real time. Predictive analytics can foresee potential dangers before they materialize, allowing companies to devise preemptive strategies.

As organizations across industries seek to leverage data to drive decision-making, improve operational efficiencies and enhance customer experiences, the demand for skilled professionals in big data analytics has surged. Here are some prominent career paths that utilize big data analytics:

Data scientist

Data scientists analyze complex digital data to assist businesses in making decisions. Using their data science training and advanced analytics technologies, including machine learning and predictive modeling, they uncover hidden insights in data.

Data analyst

Data analysts turn data into information and information into insights. They use statistical techniques to analyze and extract meaningful trends from data sets, often to inform business strategy and decisions.

Data engineer

Data engineers prepare, process and manage big data infrastructure and tools. They also develop, maintain, test and evaluate data solutions within organizations, often working with massive datasets to assist in analytics projects.

Machine learning engineer

Machine learning engineers focus on designing and implementing machine learning applications. They develop sophisticated algorithms that learn from and make predictions on data.

Business intelligence analyst

Business intelligence (BI) analysts help businesses make data-driven decisions by analyzing data to produce actionable insights. They often use BI tools to convert data into easy-to-understand reports and visualizations for business stakeholders.

Data visualization specialist

These specialists focus on the visual representation of data. They create data visualizations that help end users understand the significance of data by placing it in a visual context.

Data architect

Data architects design, create, deploy and manage an organization's data architecture. They define how data is stored, consumed, integrated and managed by different data entities and IT systems.

IBM and Cloudera have partnered to create an industry-leading, enterprise-grade big data framework distribution plus a variety of cloud services and products — all designed to achieve faster analytics at scale.

IBM Db2 Database on IBM Cloud Pak for Data combines a proven, AI-infused, enterprise-ready data management system with an integrated data and AI platform built on the security-rich, scalable Red Hat OpenShift foundation.

IBM Big Replicate is an enterprise-class data replication software platform that keeps data consistent in a distributed environment, on-premises and in the hybrid cloud, including SQL and NoSQL databases.

A data warehouse is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence and machine learning.

Business intelligence gives organizations the ability to get answers they can understand. Instead of using best guesses, they can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers or market trends.

Cloud computing is the on-demand access of physical or virtual servers, data storage, networking capabilities, application development tools, software, AI analytic tools and more—over the internet with pay-per-use pricing. The cloud computing model offers customers flexibility and scalability compared to traditional infrastructure.

Purpose-built data-driven architecture helps support business intelligence across the organization. IBM analytics solutions allow organizations to simplify raw data access, provide end-to-end data management and empower business users with AI-driven self-service analytics to predict outcomes.

Pappaya Cloud Case Study

Building share in the multi-billion cloud market with AMD

Pappaya Cloud rapidly scaled its managed services with Lenovo TruScale Infrastructure as a Service and ThinkSystem servers powered by AMD EPYC™ CPUs.

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