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Top 10 Cloud Computing Research Topics in 2020

Cloud computing has suddenly seen a spike in employment opportunities around the globe with tech giants like Amazon, Google, and Microsoft hiring people for their cloud infrastructure. Before the onset of cloud computing, companies and businesses had to set up their own data centers, allocate resources and other IT professionals thereby increasing the cost. The rapid development of the cloud has led to more flexibility, cost-cutting, and scalability. 

The Cloud Computing market its an all-time high with the current market size at USD 371.4 billion and is expected to grow up to USD 832.1 billion by 2025! It’s quickly evolving and gradually realizing its business value along with attracting more and more researchers, scholars, computer scientists, and practitioners. Cloud computing is not a single topic but a composition of various techniques which together constitute the cloud. Below are 10 the most demanded research topics in the field of cloud computing:

1. Big Data

Big data refers to the large amounts of data produced by various programs in a very short duration of time. It is quite cumbersome to store such huge and voluminous amounts of data in company-run data centers. Also, gaining insights from this data becomes a tedious task and takes a lot of time to run and provide results, therefore cloud is the best option. All the data can be pushed onto the cloud without the need for physical storage devices that are to be managed and secured. Also, some popular public clouds provide comprehensive big data platforms to turn data into actionable insights. 

DevOps is an amalgamation of two terms, Development and Operations. It has led to Continuous Delivery, Integration, and Deployment and therefore reducing boundaries between the development team and the operations team. Heavy applications and software need elaborate and complex tech stacks that demand extensive labor to develop and configure which can easily be eliminated by cloud computing. It offers a wide range of tools and technologies to build, test, and deploy applications with a few minutes and a single click. They can be customized as per the client requirements and can be discarded when not in use hence making the process seamless and cost-efficient for development teams.

3. Cloud Cryptography

Data in the cloud is needed to be protected and secured from foreign attacks and breaches. To accomplish this, cryptography in the cloud is a widely used technique to secure data present in the cloud. It allows users and clients to easily and reliably access the shared cloud services since all the data is secured using either the encryption techniques or by using the concept of the private key. It can make the plain text unreadable and limits the view of the data being transferred. Best cloud cryptographic security techniques are the ones that do not compromise the speed of data transfer and provide security without delaying the exchange of sensitive data. 

4. Cloud Load Balancing

It refers to splitting and distributing the incoming load to the server from various sources. It permits companies and organizations to govern and supervise workload demands or application demands by redistributing, reallocating, and administering resources between different computers, networks, or servers. Cloud load balancing encompasses holding the circulation of traffic and demands that exist over the Internet. This reduces the problem of sudden outages, results in an improvement in overall performance, has rare chances of server crashes, and also provides an advanced level of security. Cloud-based servers farms can accomplish more precise scalability and accessibility using the server load balancing mechanism. Due to this, the workload demands can be easily distributed and controlled.

5. Mobile Cloud Computing

It is a mixture of cloud computing, mobile computing, and wireless network to provide services such as seamless and abundant computational resources to mobile users, network operators, and cloud computing professionals. The handheld device is the console and all the processing and data storage takes place outside the physical mobile device. Some advantages of using mobile cloud computing are that there is no need for costly hardware, battery life is longer, extended data storage capacity and processing power improved synchronization of data and high availability due to “store in one place, accessible from anywhere”. The integration and security aspects are taken care of by the backend that enables support to an abundance of access methods. 

6. Green Cloud Computing

The major challenge in the cloud is the utilization of energy-efficient and hence develop economically friendly cloud computing solutions. Data centers that include servers, cables, air conditioners, networks, etc. in large numbers consume a lot of power and release enormous quantities of Carbon Dioxide in the atmosphere. Green Cloud Computing focuses on making virtual data centers and servers to be more environmentally friendly and energy-efficient. Cloud resources often consume so much power and energy leading to a shortage of energy and affecting the global climate. Green cloud computing provides solutions to make such resources more energy efficient and to reduce operational costs. This pivots on power management, virtualization of servers and data centers, recycling vast e-waste, and environmental sustainability. 

7. Edge Computing

It is the advancement and a much more efficient form of Cloud computing with the idea that the data is processed nearer to the source. Edge Computing states that all of the computation will be carried out at the edge of the network itself rather than on a centrally managed platform or the data warehouses. Edge computing distributes various data processing techniques and mechanisms across different positions. This makes the data deliverable to the nearest node and the processing at the edge. This also increases the security of the data since it is closer to the source and eliminates late response time and latency without affecting productivity.

8. Containerization

Containerization in cloud computing is a procedure to obtain operating system virtualization. The user can work with a program and its dependencies utilizing remote resource procedures. The container in cloud computing is used to construct blocks, which aid in producing operational effectiveness, version control, developer productivity, and environmental stability. The infrastructure is upgraded since it provides additional control over the granular activities over the resources. The usage of containers in online services assists storage with cloud computing data security, elasticity, and availability. Containers provide certain advantages such as a steady runtime environment, the ability to run virtually anywhere, and the low overhead compared to virtual machines. 

9. Cloud Deployment Model

There are four main cloud deployment models namely public cloud, private cloud, hybrid cloud, and community cloud. Each deployment model is defined as per the location of the infrastructure. The public cloud allows systems and services to be easily accessible to the general public. Public cloud could also be less reliable since it is open to everyone e.g. Email. A private cloud allows systems and services to be accessible inside an organization with no access to outsiders. It offers better security due to its access restrictions. Hybrid cloud is a mixture of private and public clouds with the critical activities being performed using private cloud and non-critical activities being performed using the public cloud. Community cloud allows system and services to be accessible by a group of an organization.

10. Cloud Security

Since the number of companies and organizations using cloud computing is increasing at a rapid rate, the security of the cloud is a major concern. Cloud computing security detects and addresses every physical and logical security issue that comes across all the varied service models of code, platform, and infrastructure. It collectively addresses these services, however, these services are delivered in units, that is, the public, private, or hybrid delivery model. Security in the cloud protects the data from any leakage or outflow, theft, calamity, and removal. With the help of tokenization, Virtual Private Networks, and firewalls data can be secured. 

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Latest Research Topics on Cloud Computing (2022 Updated)

Cloud computing is now a vital online technology that is used worldwide. The market size of cloud computing is expected to reach $832.1 billion by 2025 . Its demand will always increase in the future, and there are many major reasons behind it. It has acquired popularity because it is less expensive for companies rather than setting up their on-site server implementations.

In this article, we’ve covered the top 14 in-demand research topics on cloud computing that you need to know.

📌 These cloud Computing research topics are:

• Green cloud computing
• Edge computing
• Cloud cryptography
• Load balancing
• Cloud analytics
• Cloud scalability
• Mobile cloud computing
• Cloud deployment model
• Cloud security
• Cloud computing platforms
• Cloud service model
• Containerization

Top 14 Cloud Computing Research Topics For 2022

1. green cloud computing.

Due to rapid growth and demand for cloud, the energy consumption in data centers is increasing. Green Cloud Computing is used to minimize energy consumption and helps to achieve efficient processing and reduce the generation of E-waste.

 It is also called GREEN IT. The goal is to go paperless and decrease the carbon footprint in the environment due to remote working.

Power management, virtualization, sustainability, and environmental recycling will all be handled by green cloud computing. 

2. Edge Computing

A rapidly growing field where the data is processed at the network’s edge instead of being processed in a data warehouse is known as edge computing. The real-time computing capacity is driving the development of edge-computing platforms. The data is processed from the device itself to the point of origin without relying on a central location which also helps to increase the system’s security. It gives certain benefits such as cost-effectiveness, powerful performance, and new functionality which wasn’t previously available.

Some innovations are made with the help of cloud computing by increasing the ability of network edge capabilities and expanding wireless connections.

3. Cloud Cryptography

Cloud Cryptography is a strong layer of protection through codes that helps to give security to the cloud storage and breach of the data. It saves sensitive data content without delaying the transmission of information. It can turn plain text into unreadable code with the help of computers and algorithms and restrict the view of data being delivered.

The clients can use the cryptographic keys only to access this data. The user’s information is kept private, which results in fewer chances of cybercrime from the hackers. 

4. Load Balancing

The workload distribution over the server for soft computing is called load balancing. It helps distribute resources over multiple PCs, networks, and servers and allows businesses to manage workloads and application needs. Due to the rapid increase in traffic over the Internet, the server gets overloaded—two ways to solve the problem of overload of the servers: single-server and multiple-server solutions.

Keeping the system stable, boosting the system’s efficiency, and avoiding system failures are some reasons to use load balancing. It can be balanced by using software-based and hardware-based load balancers.

5. Cloud Analytics

Cloud analytics is a set of societal and analytical tools that analyze data on a private or public cloud to reduce data storage costs and management. It is specially designed to help clients get information from massive data. It is widely used in industrial applications such as genomics research, oil and gas exploration, business intelligence, security, and the Internet of Things (IoT).

It can help any industry improve its organizational performance and drive new value from its data. It is delivered through various models: public, private, hybrid, and community models. 

6. Cloud Scalability

Cloud scalability refers to the capacity to scale up or down IT resources as per the need for change in computing. Scalability is usually used to fulfill the static needs where the workload is handled linearly when resource deployment is persistent.

The types of scalability are vertical, horizontal, and diagonal. Horizontal scaling is regarded as a long-term advantage; on the other hand, vertical scaling is considered a short-term advantage. The benefits of cloud scalability are reliability, cost-effectiveness, ease, and speed. It is critical to understand how much those changes will cost and how they will benefit the company.

It can be applied to Disk I/O, Memory, Network I/O, and CPU. 

7. Mobile Cloud Computing

Mobile cloud computing helps to deliver applications to mobile devices through cloud computing. It allows different devices with different operating systems to have operating systems, computing tasks, and data storage. Mobile cloud helps speed and flexibility, resource sharing, and integrated data. Mobile Cloud Computing advantages are:

• Increased battery life
• Improvement in reliability and scalability
• Simple Integration
• Low cost and data storage capacity
• Processing power improvement

The only drawback is that the bandwidth and variability are limited. It has been chosen due to productivity and demand, increasing connectivity.

8. Big Data

Big data is a technology generated by large network-based systems with massive amounts of data produced by different sources. The data get classified through structured (organized data) and unstructured (unorganized data), and semi-structured forms. The data are analyzed through algorithms which may vary depending upon the data means. Its characteristics are Volume, Variety, Velocity, and Variability.

Organizations can make better decisions with the help of external intelligence, which includes improvements in customer service, evaluation of consumer feedback, and identification of any risks to the product/services.

9. Cloud Deployment Model

The way people use the cloud has evolved based on ownership, scalability, access, and the cloud’s nature and purpose. A cloud deployment model identifies a particular sort of cloud environment that determines the cloud infrastructure’s appearance.

Cloud computing deployment models are classified according to their geographical location. Deployment methods are available in public, private, hybrid, community, and multi-cloud models.

It depends on the firms to choose as per their requirements as each model has its unique value and contribution.

10. Cloud Security

Cloud security brings the revolution to the current business model through shifts in information technology. With the rapid increase in the number of cloud computing, the organization needs the security of the cloud, which has become a significant concern.

Cloud Security protects the data from any leakage or outflow, with the removal of theft and catastrophe. The cloud has public, private, and hybrid clouds for security purposes.

Cloud security is needed to secure clients’ data, such as secret design documents and financial records. Its benefits are lower costs, reduced ongoing operational and administrative expenses, increased data reliability and availability, and reduced administration.

11. Cloud Computing Platforms

In an Internet-based data center, a server’s operating system and hardware are referred to as a cloud platform. Cloud platforms work when a firm rents to access computer services, such as servers, databases, storage, analytics, networking, software, and intelligence. So the companies don’t have to set up their data centers or computing infrastructure; they need to pay for what they use. It is a very vast platform where we can do many types of research.

12. Cloud Service Model

The use of networks hosted on the Internet to store from remote servers used in managing and processing data, rather than from a local server or a personal computer. It has three models namely Infrastructure-as-a-Service (IaaS), Software-as-a-Service (SaaS),and Platform-as-a-Service (PaaS).Each type of cloud computing service provides different control, flexibility, and management levels to choose the right services for your requirements.

The ability to deliver applications and services increases an organization’s ability to evolve and improve products faster. This model helps the firms have their benefits more quickly and better than traditional software. In the DevOps approach, development and operations teams are integrated into a single unit, enabling them to develop diverse skills that aren’t limited to a particular task. The benefits of DevOps are rapidity, increase in frequency, reliability, scale, improved collaboration, and security.

It provides a wide range of tools and technologies to meet clients’ needs.

14. Containerization

Containerization is a popular software development technique that is rapidly evolving and can be used in addition to virtualization. It includes packaging software code and all of its components so that it may run consistently and uniformly across any infrastructure. The developers and operational teams see its benefit as it helps create and locate applications quickly and more securely. It benefits developers and development groups as it provides flexibility/ portability, the ability to move swiftly and efficiently, speed, fault isolation, efficiency, easily manageable, and security. 

Final Words

Hence, all the above are new technologies of cloud computing developed to benefit users worldwide. But there are some challenges that need to be overcome. People nowadays have become skeptical about whether their data is private, secure, or not. This research can make this security more advanced and help to provide innovations in cloud computing.

We hope this article helps you to know some best research topics on cloud computing and how they’re changing the world.

10Pie Editorial Team is a team of certified technical content writers and editors with experience in the technology field combined with expert insights . Learn more about our editorial process to ensure the quality and accuracy of the content published on our website.

10Pie is your go-to resource hub to start and grow your Tech Career.

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Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

<span lang="EN-US">Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms.</span>

An improved forensic-by-design framework for cloud computing with systems engineering standard compliance

Reliability of trust management systems in cloud computing.

Cloud computing is an innovation that conveys administrations like programming, stage, and framework over the web. This computing structure is wide spread and dynamic, which chips away at the compensation per-utilize model and supports virtualization. Distributed computing is expanding quickly among purchasers and has many organizations that offer types of assistance through the web. It gives an adaptable and on-request administration yet at the same time has different security dangers. Its dynamic nature makes it tweaked according to client and supplier’s necessities, subsequently making it an outstanding benefit of distributed computing. However, then again, this additionally makes trust issues and or issues like security, protection, personality, and legitimacy. In this way, the huge test in the cloud climate is selecting a perfect organization. For this, the trust component assumes a critical part, in view of the assessment of QoS and Feedback rating. Nonetheless, different difficulties are as yet present in the trust the board framework for observing and assessing the QoS. This paper talks about the current obstructions present in the trust framework. The objective of this paper is to audit the available trust models. The issues like insufficient trust between the supplier and client have made issues in information sharing likewise tended to here. Besides, it lays the limits and their enhancements to help specialists who mean to investigate this point.

Cloud Computing Adoption in the Construction Industry of Singapore: Drivers, Challenges, and Strategies

An extensive review of web-based multi granularity service composition.

The paper reviews the efforts to compose SOAP, non-SOAP and non-web services. Traditionally efforts were made for composite SOAP services, however, these efforts did not include the RESTful and non-web services. A SOAP service uses structured exchange methodology for dealing with web services while a non-SOAP follows different approach. The research paper reviews the invoking and composing a combination of SOAP, non-SOAP, and non-web services into a composite process to execute complex tasks on various devices. It also shows the systematic integration of the SOAP, non-SOAP and non-web services describing the composition of heterogeneous services than the ones conventionally used from the perspective of resource consumption. The paper further compares and reviews different layout model for the discovery of services, selection of services and composition of services in Cloud computing. Recent research trends in service composition are identified and then research about microservices are evaluated and shown in the form of table and graphs.

Integrated Blockchain and Cloud Computing Systems: A Systematic Survey, Solutions, and Challenges

Cloud computing is a network model of on-demand access for sharing configurable computing resource pools. Compared with conventional service architectures, cloud computing introduces new security challenges in secure service management and control, privacy protection, data integrity protection in distributed databases, data backup, and synchronization. Blockchain can be leveraged to address these challenges, partly due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. We present a comprehensive survey of how blockchain is applied to provide security services in the cloud computing model and we analyze the research trends of blockchain-related techniques in current cloud computing models. During the reviewing, we also briefly investigate how cloud computing can affect blockchain, especially about the performance improvements that cloud computing can provide for the blockchain. Our contributions include the following: (i) summarizing the possible architectures and models of the integration of blockchain and cloud computing and the roles of cloud computing in blockchain; (ii) classifying and discussing recent, relevant works based on different blockchain-based security services in the cloud computing model; (iii) simply investigating what improvements cloud computing can provide for the blockchain; (iv) introducing the current development status of the industry/major cloud providers in the direction of combining cloud and blockchain; (v) analyzing the main barriers and challenges of integrated blockchain and cloud computing systems; and (vi) providing recommendations for future research and improvement on the integration of blockchain and cloud systems.

Cloud Computing and Undergraduate Researches in Universities in Enugu State: Implication for Skills Demand

Cloud building block chip for creating fpga and asic clouds.

Hardware-accelerated cloud computing systems based on FPGA chips (FPGA cloud) or ASIC chips (ASIC cloud) have emerged as a new technology trend for power-efficient acceleration of various software applications. However, the operating systems and hypervisors currently used in cloud computing will lead to power, performance, and scalability problems in an exascale cloud computing environment. Consequently, the present study proposes a parallel hardware hypervisor system that is implemented entirely in special-purpose hardware, and that virtualizes application-specific multi-chip supercomputers, to enable virtual supercomputers to share available FPGA and ASIC resources in a cloud system. In addition to the virtualization of multi-chip supercomputers, the system’s other unique features include simultaneous migration of multiple communicating hardware tasks, and on-demand increase or decrease of hardware resources allocated to a virtual supercomputer. Partitioning the flat hardware design of the proposed hypervisor system into multiple partitions and applying the chip unioning technique to its partitions, the present study introduces a cloud building block chip that can be used to create FPGA or ASIC clouds as well. Single-chip and multi-chip verification studies have been done to verify the functional correctness of the hypervisor system, which consumes only a fraction of (10%) hardware resources.

Study On Social Network Recommendation Service Method Based On Mobile Cloud Computing

Cloud-based network virtualization in iot with openstack.

In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.

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Future of cloud computing: 5 insights from new global research

Carol Carpenter

VP of Cloud Product Marketing

Research shows that cloud computing will transform every aspect of business, from logistics to customer relationships to the way teams work together, and today’s organizations are preparing for this seismic shift. A new report from Google on the future of cloud computing combines an in-depth look at how the cloud is shaping the enterprise of tomorrow with actionable advice to help today’s leaders unlock its benefits. Along with insights from Google luminaries and leading companies, the report includes key findings from a research study that surveyed 1,100 business and IT decision-makers from around the world. Their responses shed light on the rapidly evolving technology landscape at a global level, as well as variations in cloud maturity and adoption trends across individual countries. Here are five themes that stood out to us from this brand-new research.

1. Cloud computing will move to the forefront of enterprise technology over the next decade, backed by strong executive support.

Globally, 47 percent of survey participants said that the majority of their companies’ IT infrastructures already use public or private cloud computing. When we asked about predictions for 2029, that number jumped 30 percentage points. C-suite respondents were especially confident that the cloud will reign supreme within a decade: More than half anticipate that it will meet at least three-quarters of their IT needs, while only 40 percent of their non-C-suite peers share that view. What’s the takeaway? The cloud already plays a key role in enterprise technology, but the next 10 years will see it move to the forefront—with plenty of executive support. Here’s how that data breaks down around the world.

2. The cloud is becoming a significant driver of revenue growth.

Cloud computing helps businesses focus on improving efficiency and fostering innovation, not simply maintaining systems and status quos. So it’s not surprising that 79 percent of survey respondents already consider the cloud an important driver of revenue growth, while 87 percent expect it to become one within a decade. C-suite respondents were just as likely as their non-C-suite peers to anticipate that the cloud will play an important role in driving revenue growth in 2029. This tells us that decision-makers across global organizations believe their future success will hinge on their ability to effectively apply cloud technology.

3. Businesses are combining cloud capabilities with edge computing to analyze data at its source.

Over the next decade, the cloud will continue to evolve as part of a technology stack that increasingly includes IoT devices and edge computing, in which processing occurs at or near the data’s source. Thirty-three percent of global respondents said they use edge computing for a majority of their cloud operations, while 55 percent expect to do so by 2029. The United States lags behind in this area, with only 18 percent of survey participants currently using edge computing for a majority of their cloud operations, but that figure grew by a factor of 2.5 when respondents looked ahead to 2029. As more and more businesses extend the power and intelligence of the cloud to the edge, we can expect to see better real-time predictions, faster responses, and more seamless customer experiences.

4. Tomorrow’s businesses will prioritize openness and interoperability.

In the best cases, cloud adoption is part of a larger transformation in which new tools and systems positively affect company culture. Our research suggests that businesses will continue to place more value on openness over the next decade. By 2029, 41 percent of global respondents expect to use open-source software (OSS) for a majority of their software platform, up 14 percentage points from today. Predicted OSS use was nearly identical between IT decision-makers and their business-oriented peers, implying that technology and business leaders alike recognize the value of interoperability, standardization, freedom from vendor lock-in, and continuous innovation.

5. On their journey to the cloud, companies are using new techniques to balance speed and quality.

To stay competitive in today’s streaming world, businesses face growing pressure to innovate faster—and the cloud is helping them keep pace. Sixty percent of respondents said their companies will update code weekly or daily by 2029, while 37 percent said they’ve already adopted this approach. This tells us that over the next 10 years, we’ll see an uptick in the use of continuous integration and delivery techniques, resulting in more frequent releases and higher developer productivity.

As organizations prepare for the future, they will need to balance the need for speed with maintaining high quality. Our research suggests that they’ll do so by addressing security early in the development process and assuming constant vulnerability so they’re never surprised. More than half of respondents said they already implement security pre-development, and 72 percent plan to do so by 2029.

Cloud-based enterprises will also rely on automation to maintain quality and security as their operations become faster and more continuous. Seventy percent of respondents expect a majority of their security operations to be automated by 2029, compared to 33 percent today.

Our Future of Cloud Computing report contains even more insights from our original research, as well as a thorough analysis of the cloud’s impact on businesses and recommended steps for unlocking its full potential. You can download it here .

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Journal of Cloud Computing

Advances, Systems and Applications

Special Issues - Guidelines for Guest Editors

For more information for Guest Editors, please see our Guidelines

Special Issues - Call for Papers

We welcome submissions for the upcoming special issues of the Journal of Cloud Computing

Advanced Blockchain and Federated Learning Techniques Towards Secure Cloud Computing Guest Editors: Yuan Liu, Jie Zhang, Athirai A. Irissappane, Zhu Sun Submission deadline:  30 April 2024

Mobile Edge Computing Meets AI Guest Editors: Lianyong Qi, Maqbool Khan, Qiang He, Shui Yu, Wajid Rafique Submission deadline:  3 May 2024   Blockchain-enabled Decentralized Cloud/Edge Computing Guest Editors: Qingqi Pei, Kaoru Ota, Martin Gilje Jaatun, Jie Feng, Shen Su Submission deadline: 31 st  March 2023

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Aims and scope

The Journal of Cloud Computing: Advances, Systems and Applications (JoCCASA) will publish research articles on all aspects of Cloud Computing. Principally, articles will address topics that are core to Cloud Computing, focusing on the Cloud applications, the Cloud systems, and the advances that will lead to the Clouds of the future. Comprehensive review and survey articles that offer up new insights, and lay the foundations for further exploratory and experimental work, are also relevant.

Published articles will impart advanced theoretical grounding and practical application of Clouds and related systems as are offered up by the numerous possible combinations of internet-based software, development stacks and database availability, and virtualized hardware for storing, processing, analysing and visualizing data. Where relevant, Clouds should be scrutinized alongside other paradigms such Peer to Peer (P2P) computing, Cluster computing, Grid computing, and so on. Thorough examination of Clouds with respect to issues of management, governance, trust and privacy, and interoperability, are also in scope. The Journal of Cloud Computing is indexed by the Science Citation Index Expanded/SCIE. SCI has subsequently merged into SCIE.  

Cloud Computing is now a topic of significant impact and, while it may represent an evolution in technology terms, it is revolutionising the ways in which both academia and industry are thinking and acting. The Journal of Cloud Computing, Advances, Systems and Applications (JoCCASA) has been launched to offer a high quality journal geared entirely towards the research that will offer up future generations of Clouds. The journal publishes research that addresses the entire Cloud stack, and as relates Clouds to wider paradigms and topics.

Chunming Rong, Editor-in-Chief University of Stavanger, Norway

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Top 10 Cloud Computing Research Topics in 2022

Table of contents.

Cloud computing as a technology may have been in the cards for a long time, but its widespread application and popularity have increased in recent times. Moreover, at its current size, this industry is valued at approximately $850 billion. However, this number will not hold on for long as it is likely to go up in the coming years.

Nonetheless, if you are interested in this field and willing to learn more about it, here are 10 research topics on cloud computing that can help you start.

Top 10 Research Topics for Cloud Computing in 2022

Here are ten research topics for cloud computing to look forward to in 2022 –

• Cloud analytics

Cloud analytics is a cloud-related analytical tool that helps to analyze data and reduce data storage costs. It is used for research in genomics, exploring oil and gas reserves, business intelligence, Internet of Things (IoT) and cybersecurity. It unleashes the power of data to improve the organizational performance of a company.

• Load balancing

The workload distribution for soft computing over the server is known as load balancing. It helps in the distribution of resources over various local servers, networks and industrial servers for workload management and requirement of applications, and it also helps to keep the system stable and boost its efficiency so that there is no malfunctioning or failure of any type.

• Green cloud computing

The consumption of energy consumption is increasing in data centres due to an increase in demand for cloud services. Green cloud computing will help to minimise the consumption of energy and reduce e-waste generation. Management of power, virtualisation of the system along with the computation of the system sustainability, and recycling of environmental resources will be handled by green cloud computing systems.

• Edge computing

Processing of data at the edge of a network instead of a data warehouse is called edge computing. Some innovations are possible only due to cloud computing, which amplifies a network edge's capabilities and helps expand the domain of wireless connections.

• Cloud cryptography

Cloud cryptography adds strong protection layers which help in giving security to the cloud storage infrastructure. It helps to prevent the breach of data by saving sensitive data containing any information transmitted to third parties. Cloud cryptography systems convert plain text into an unreadable form of code. It is helped by computers and algorithms that restrict the preview of data during its delivery.

• Cloud scalability

Cloud scalability is the capability of scaling the IT resources over the cloud up or down as per the computing changes requirements. A system can be scaled horizontally, diagonally and vertically. Scalability can be applied to Memory and Disk I/O, CPU and Network I/O.

• Mobile cloud computing

These refer to the cloud computing systems that are typically for the Mobile computing system, which allows different OS, computing tasks, and data storage. Mobile cloud has many advantages. It increases the speed and flexibility of the system. It enables resource sharing across multiple systems. Mobile Cloud Computing helps in the integration of data.

Big data is the technology that helps handle large network-based systems with copious amounts from different sources. All unstructured data is connected to structured data and organised in a particular way so that handling it becomes hassle-free. Moreover, it becomes easy to manage them from one dashboard. A lot of innovation is going into this field.

• Cloud deployment model

Nowadays, a lot of apps are hosted and stored on cloud systems. So for each type of application, there needs to be a model which is based on scalability, access, scalability, ownership, cloud nature and purpose of the deployment. A cloud deployment model helps to find out which cloud environment determines the infrastructure of the cloud that suits the system best.

DevOps is all about delivering apps and services that enhance an organisation’s product, making it better and faster. The research in DevOps can help to achieve advanced security in cloud computing systems.

To conclude, this write-up has offered much-needed clarity regarding the cloud computing research topics that are popular nowadays. Hopefully, it will help you find your niche, get a more in-depth understanding of the topic, and build your career around it.

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A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

• Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

• MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
• CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
• CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
• Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

• Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

• Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

• Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems . There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

• Highly calibrated cameras that take a photograph of the image from various angles.
• Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

• Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

• Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

• Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

• 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
• It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
• They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
• It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
• It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website . 

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

• Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
• Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
• Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
• Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

• The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
• Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
• GAUDI also removes GANs pathologies like mode collapse and improved GAN.
• GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

• Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
• This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
• The scenes are thus synthesized by interpolation within the hidden space.
• The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
• A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/  

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html

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Top 10 Cloud Computing Research Topics of 2024

Home Blog Cloud Computing Top 10 Cloud Computing Research Topics of 2024

Cloud computing is a fast-growing area in the technical landscape due to its recent developments. If we look ahead to 2024, there are new research topics in cloud computing that are getting more traction among researchers and practitioners. Cloud computing has ranged from new evolutions on security and privacy with the use of AI & ML usage in the Cloud computing for the new cloud-based applications for specific domains or industries. In this article, we will investigate some of the top cloud computing research topics for 2024 and explore what we get most out of it for researchers or cloud practitioners. To master a cloud computing field, we need to check these Cloud Computing online courses .

Why Cloud Computing is Important for Data-driven Business?

The Cloud computing is crucial for data-driven businesses because it provides scalable and cost-effective ways to store and process huge amounts of data. Cloud-based storage and analytical platform helps business to easily access their data whenever required irrespective of where it is located physically. This helps businesses to take good decisions about their products and marketing plans. 

Cloud computing could help businesses to improve their security in terms of data, Cloud providers offer various features such as data encryption and access control to their customers so that they can protect the data as well as from unauthorized access. 

Few benefits of Cloud computing are listed below: 

• Scalability: With Cloud computing we get scalable applications which suits for large scale production systems for Businesses which store and process large sets of data.
• Cost-effectiveness : It is evident that Cloud computing is cost effective solution compared to the traditional on-premises data storage and analytical solutions due to its scaling capacity which leads to saving more IT costs. 
• Security : Cloud providers offer various security features which includes data encryption and access control, that can help businesses to protect their data from unauthorized access.
• Reliability : Cloud providers ensure high reliability to their customers based on their SLA which is useful for the data-driven business to operate 24X7. 

Top 10 Cloud Computing Research Topics

1. neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing.

Cloud computing research topics are getting wider traction in the Cloud Computing field. These topics in the paper suggest a multi-objective evolutionary algorithm (NN-MOEA) based on neural networks for dynamic workflow scheduling in cloud computing. Due to the dynamic nature of cloud resources and the numerous competing objectives that need to be optimized, scheduling workflows in cloud computing is difficult. The NN-MOEA algorithm utilizes neural networks to optimize multiple objectives, such as planning, cost, and resource utilization. This research focuses on cloud computing and its potential to enhance the efficiency and effectiveness of businesses' cloud-based workflows.

The algorithm predicts workflow completion time using a feedforward neural network based on input and output data sizes and cloud resources. It generates a balanced schedule by taking into account conflicting objectives and projected execution time. It also includes an evolutionary algorithm for future improvement.

The proposed NN-MOEA algorithm has several benefits, such as the capacity to manage dynamic changes in cloud resources and the capacity to simultaneously optimize multiple objectives. The algorithm is also capable of handling a variety of workflows and is easily expandable to include additional goals. The algorithm's use of neural networks to forecast task execution times is a crucial component because it enables the algorithm to generate better schedules and more accurate predictions.

The paper concludes by presenting a novel multi-objective evolutionary algorithm-based neural network-based approach to dynamic workflow scheduling in cloud computing. In terms of optimizing multiple objectives, such as make span and cost, and achieving a better balance between them, these cloud computing dissertation topics on the proposed NN-MOEA algorithm exhibit encouraging results.

Key insights and Research Ideas:

Investigate the use of different neural network architectures for predicting the future positions of optimal solutions. Explore the use of different multi-objective evolutionary algorithms for solving dynamic workflow scheduling problems. Develop a cloud-based workflow scheduling platform that implements the proposed algorithm and makes it available to researchers and practitioners.

2. A systematic literature review on cloud computing security: threats and mitigation strategies 

This is one of cloud computing security research topics in the cloud computing paradigm. The authors then provide a systematic literature review of studies that address security threats to cloud computing and mitigation techniques and were published between 2010 and 2020. They list and classify the risks and defense mechanisms covered in the literature, as well as the frequency and distribution of these subjects over time.

The paper suggests the data breaches, Insider threats and DDoS attack are most discussed threats to the security of cloud computing. Identity and access management, encryption, and intrusion detection and prevention systems are the mitigation techniques that are most frequently discussed. Authors depict the future trends of machine learning and artificial intelligence might help cloud computing to mitigate its risks. 

The paper offers a thorough overview of security risks and mitigation techniques in cloud computing, and it emphasizes the need for more research and development in this field to address the constantly changing security issues with cloud computing. This research could help businesses to reduce the amount of spam that they receive in their cloud-based email systems.

Explore the use of blockchain technology to improve the security of cloud computing systems. Investigate the use of machine learning and artificial intelligence to detect and prevent cloud computing attacks. Develop new security tools and technologies for cloud computing environments. 

3. Spam Identification in Cloud Computing Based on Text Filtering System

A text filtering system is suggested in the paper "Spam Identification in Cloud Computing Based on Text Filtering System" to help identify spam emails in cloud computing environments. Spam emails are a significant issue in cloud computing because they can use up computing resources and jeopardize the system's security. 

To detect spam emails, the suggested system combines text filtering methods with machine learning algorithms. The email content is first pre-processed by the system, which eliminates stop words and stems the remaining words. The preprocessed text is then subjected to several filters, including a blacklist filter and a Bayesian filter, to identify spam emails.

In order to categorize emails as spam or non-spam based on their content, the system also employs machine learning algorithms like decision trees and random forests. The authors use a dataset of emails gathered from a cloud computing environment to train and test the system. They then assess its performance using metrics like precision, recall, and F1 score.

The findings demonstrate the effectiveness of the proposed system in detecting spam emails, achieving high precision and recall rates. By contrasting their system with other spam identification systems, the authors also show how accurate and effective it is. 

The method presented in the paper for locating spam emails in cloud computing environments has the potential to improve the overall security and performance of cloud computing systems. This is one of the interesting clouds computing current research topics to explore and innovate. This is one of the good Cloud computing research topics to protect the Mail threats. 

Create a stronger spam filtering system that can recognize spam emails even when they are made to avoid detection by more common spam filters. examine the application of artificial intelligence and machine learning to the evaluation of spam filtering system accuracy. Create a more effective spam filtering system that can handle a lot of emails quickly and accurately.

4. Blockchain data-based cloud data integrity protection mechanism 

The "Blockchain data-based cloud data integrity protection mechanism" paper suggests a method for safeguarding the integrity of cloud data and which is one of the Cloud computing research topics. In order to store and process massive amounts of data, cloud computing has grown in popularity, but issues with data security and integrity still exist. For the proposed mechanism to guarantee the availability and integrity of cloud data, data redundancy and blockchain technology are combined.

A data redundancy layer, a blockchain layer, and a verification and recovery layer make up the mechanism. For availability in the event of server failure, the data redundancy layer replicates the cloud data across multiple cloud servers. The blockchain layer stores the metadata (such as access rights) and hash values of the cloud data and access control information

Using a dataset of cloud data, the authors assess the performance of the suggested mechanism and compare it to other cloud data protection mechanisms. The findings demonstrate that the suggested mechanism offers high levels of data availability and integrity and is superior to other mechanisms in terms of processing speed and storage space.

Overall, the paper offers a promising strategy for using blockchain technology to guarantee the availability and integrity of cloud data. The suggested mechanism may assist in addressing cloud computing's security issues and enhancing the dependability of cloud data processing and storage. This research could help businesses to protect the integrity of their cloud-based data from unauthorized access and manipulation.

Create a data integrity protection system based on blockchain that is capable of detecting and preventing data tampering in cloud computing environments. For enhancing the functionality and scalability of blockchain-based data integrity protection mechanisms, look into the use of various blockchain consensus algorithms. Create a data integrity protection system based on blockchain that is compatible with current cloud computing platforms. Create a safe and private data integrity protection system based on blockchain technology.

5. A survey on internet of things and cloud computing for healthcare

This article suggests how recent tech trends like the Internet of Things (IoT) and cloud computing could transform the healthcare industry. It is one of the Cloud computing research topics. These emerging technologies open exciting possibilities by enabling remote patient monitoring, personalized care, and efficient data management. This topic is one of the IoT and cloud computing research papers which aims to share a wider range of information. 

The authors categorize the research into IoT-based systems, cloud-based systems, and integrated systems using both IoT and the cloud. They discussed the pros of real-time data collection, improved care coordination, automated diagnosis and treatment.

However, the authors also acknowledge concerns around data security, privacy, and the need for standardized protocols and platforms. Widespread adoption of these technologies faces challenges in ensuring they are implemented responsibly and ethically. To begin the journey KnowledgeHut’s Cloud Computing online course s are good starter for beginners so that they can cope with Cloud computing with IOT. 

Overall, the paper provides a comprehensive overview of this rapidly developing field, highlighting opportunities to revolutionize how healthcare is delivered. New devices, systems and data analytics powered by IoT, and cloud computing could enable more proactive, preventative and affordable care in the future. But careful planning and governance will be crucial to maximize the value of these technologies while mitigating risks to patient safety, trust and autonomy. This research could help businesses to explore the potential of IoT and cloud computing to improve healthcare delivery.

Examine how IoT and cloud computing are affecting patient outcomes in various healthcare settings, including hospitals, clinics, and home care. Analyze how well various IoT devices and cloud computing platforms perform in-the-moment patient data collection, archival, and analysis. assessing the security and privacy risks connected to IoT devices and cloud computing in the healthcare industry and developing mitigation strategies.

6. Targeted influence maximization based on cloud computing over big data in social networks

Big data in cloud computing research papers are having huge visibility in the industry. The paper "Targeted Influence Maximization based on Cloud Computing over Big Data in Social Networks" proposes a targeted influence maximization algorithm to identify the most influential users in a social network. Influence maximization is the process of identifying a group of users in a social network who can have a significant impact or spread information. 

A targeted influence maximization algorithm is suggested in the paper "Targeted Influence maximization based on Cloud Computing over Big Data in Social Networks" to find the most influential users in a social network. The process of finding a group of users in a social network who can make a significant impact or spread information is known as influence maximization.

Four steps make up the suggested algorithm: feature extraction, classification, influence maximization, and data preprocessing. The authors gather and preprocess social network data, such as user profiles and interaction data, during the data preprocessing stage. Using machine learning methods like text mining and sentiment analysis, they extract features from the data during the feature extraction stage. Overall, the paper offers a promising strategy for maximizing targeted influence using big data and Cloud computing research topics to look into. The suggested algorithm could assist companies and organizations in pinpointing their marketing or communication strategies to reach the most influential members of a social network.

Key insights and Research Ideas: 

Develop a cloud-based targeted influence maximization algorithm that can effectively identify and influence a small number of users in a social network to achieve a desired outcome. Investigate the use of different cloud computing platforms to improve the performance and scalability of cloud-based targeted influence maximization algorithms. Develop a cloud-based targeted influence maximization algorithm that is compatible with existing social network platforms. Design a cloud-based targeted influence maximization algorithm that is secure and privacy-preserving.

7. Security and privacy protection in cloud computing: Discussions and challenges

Cloud computing current research topics are getting traction, this is of such topic which provides an overview of the challenges and discussions surrounding security and privacy protection in cloud computing. The authors highlight the importance of protecting sensitive data in the cloud, with the potential risks and threats to data privacy and security. The article explores various security and privacy issues that arise in cloud computing, including data breaches, insider threats, and regulatory compliance.

The article explores challenges associated with implementing these security measures and highlights the need for effective risk management strategies. Azure Solution Architect Certification course is suitable for a person who needs to work on Azure cloud as an architect who will do system design with keep security in mind. 

Final take away of cloud computing thesis paper by an author points out by discussing some of the emerging trends in cloud security and privacy, including the use of artificial intelligence and machine learning to enhance security, and the emergence of new regulatory frameworks designed to protect data in the cloud and is one of the Cloud computing research topics to keep an eye in the security domain. 

Develop a more comprehensive security and privacy framework for cloud computing. Explore the options with machine learning and artificial intelligence to enhance the security and privacy of cloud computing. Develop more robust security and privacy mechanisms for cloud computing. Design security and privacy policies for cloud computing that are fair and transparent. Educate cloud users about security and privacy risks and best practices.

8. Intelligent task prediction and computation offloading based on mobile-edge cloud computing

This Cloud Computing thesis paper "Intelligent Task Prediction and Computation Offloading Based on Mobile-Edge Cloud Computing" proposes a task prediction and computation offloading mechanism to improve the performance of mobile applications under the umbrella of cloud computing research ideas.

An algorithm for offloading computations and a task prediction model makes up the two main parts of the suggested mechanism. Based on the mobile application's usage patterns, the task prediction model employs machine learning techniques to forecast its upcoming tasks. This prediction is to decide whether to execute a specific task locally on the mobile device or offload the computation of it to the cloud.

Using a dataset of mobile application usage patterns, the authors assess the performance of the suggested mechanism and compare it to other computation offloading mechanisms. The findings demonstrate that the suggested mechanism performs better in terms of energy usage, response time, and network usage.

The authors also go over the difficulties in putting the suggested mechanism into practice, including the need for real-time task prediction and the trade-off between offloading computation and network usage. Additionally, they outline future research directions for mobile-edge cloud computing applications, including the use of edge caching and the integration of blockchain technology for security and privacy. 

Overall, the paper offers a promising strategy for enhancing mobile application performance through mobile-edge cloud computing. The suggested mechanism might improve the user experience for mobile users while lowering the energy consumption and response time of mobile applications. These Cloud computing dissertation topic leads to many innovation ideas. 

Develop an accurate task prediction model considering mobile device and cloud dynamics. Explore machine learning and AI for efficient computation offloading. Create a robust framework for diverse tasks and scenarios. Design a secure, privacy-preserving computation offloading mechanism. Assess computation offloading effectiveness in real-world mobile apps.

9. Cloud Computing and Security: The Security Mechanism and Pillars of ERPs on Cloud Technology

Enterprise resource planning (ERP) systems are one of the Cloud computing research topics in particular face security challenges with cloud computing, and the paper "Cloud Computing and Security: The Security Mechanism and Pillars of ERPs on Cloud Technology" discusses these challenges and suggests a security mechanism and pillars for protecting ERP systems on cloud technology.

The authors begin by going over the benefits of ERP systems and cloud computing as well as the security issues with cloud computing, like data breaches and insider threats. They then go on to present a security framework for cloud-based ERP systems that is built around four pillars: access control, data encryption, data backup and recovery, and security monitoring. The access control pillar restricts user access, while the data encryption pillar secures sensitive data. Data backup and recovery involve backing up lost or failed data. Security monitoring continuously monitors the ERP system for threats. The authors also discuss interoperability challenges and the need for standardization in securing ERP systems on the cloud. They propose future research directions, such as applying machine learning and artificial intelligence to security analytics.

Overall, the paper outlines a thorough strategy for safeguarding ERP systems using cloud computing and emphasizes the significance of addressing security issues related to this technology. Organizations can protect their ERP systems and make sure the Security as well as privacy of their data by implementing these security pillars and mechanisms. 

Investigate the application of blockchain technology to enhance the security of cloud-based ERP systems. Look into the use of machine learning and artificial intelligence to identify and stop security threats in cloud-based ERP systems. Create fresh security measures that are intended only for cloud-based ERP systems. By more effectively managing access control and data encryption, cloud-based ERP systems can be made more secure. Inform ERP users about the security dangers that come with cloud-based ERP systems and how to avoid them.

10. Optimized data storage algorithm of IoT based on cloud computing in distributed system

The article proposes an optimized data storage algorithm for Internet of Things (IoT) devices which runs on cloud computing in a distributed system. In IoT apps, which normally generate huge amounts of data by various devices, the algorithm tries to increase the data storage and faster retrials of the same. 

The algorithm proposed includes three main components: Data Processing, Data Storage, and Data Retrieval. The Data Processing module preprocesses IoT device data by filtering or compressing it. The Data Storage module distributes the preprocessed data across cloud servers using partitioning and stores it in a distributed database. The Data Retrieval module efficiently retrieves stored data in response to user queries, minimizing data transmission and enhancing query efficiency. The authors evaluated the algorithm's performance using an IoT dataset and compared it to other storage and retrieval algorithms. Results show that the proposed algorithm surpasses others in terms of storage effectiveness, query response time, and network usage. 

They suggest future directions such as leveraging edge computing and blockchain technology for optimizing data storage and retrieval in IoT applications. In conclusion, the paper introduces a promising method to improve data archival and retrieval in distributed cloud based IoT applications, enhancing the effectiveness and scalability of IoT applications.

Create a data storage algorithm capable of storing and managing large amounts of IoT data efficiently. Examine the use of cloud computing to improve the performance and scalability of data storage algorithms for IoT. Create a secure and privacy-preserving data storage algorithm. Assess the performance and effectiveness of data storage algorithms for IoT in real-world applications.

How to Write a Perfect Research Paper?

• Choose a topic: Select the topic which is interesting to you so that you can share things with the viewer seamlessly with good content. 
• Do your research: Read books, articles, and websites on your topic. Take notes and gather evidence to support your arguments.
• Write an outline: This will help you organize your thoughts and make sure your paper flows smoothly.
• Start your paper: Start with an introduction that grabs the reader's attention. Then, state your thesis statement and support it with evidence from your research. Finally, write a conclusion that summarizes your main points.
• Edit and proofread your paper. Make sure you check the grammatical errors and spelling mistakes. 

Cloud computing is a rapidly evolving area with more interesting research topics being getting traction by researchers and practitioners. Cloud providers have their research to make sure their customer data is secured and take care of their security which includes encryption algorithms, improved access control and mitigating DDoS – Deniel of Service attack etc., 

With the improvements in AI & ML, a few features developed to improve the performance, efficiency, and security of cloud computing systems. Some of the research topics in this area include developing new algorithms for resource allocation, optimizing cloud workflows, and detecting and mitigating cyberattacks.

Cloud computing is being used in industries such as healthcare, finance, and manufacturing. Some of the research topics in this area include developing new cloud-based medical imaging applications, building cloud-based financial trading platforms, and designing cloud-based manufacturing systems.

Frequently Asked Questions (FAQs)

Data security and privacy problems, vendor lock-in, complex cloud management, a lack of standardization, and the risk of service provider disruptions are all current issues in cloud computing. Because data is housed on third-party servers, data security and privacy are key considerations. Vendor lock-in makes transferring providers harder and increases reliance on a single one. Managing many cloud services complicates things. Lack of standardization causes interoperability problems and restricts workload mobility between providers. 

Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are the cloud computing scenarios where industries focusing right now. 

The six major components of cloud infrastructure are compute, storage, networking, security, management and monitoring, and database. These components enable cloud-based processing and execution, data storage and retrieval, communication between components, security measures, management and monitoring of the infrastructure, and database services.  

Vinoth Kumar P

Vinoth Kumar P is a Cloud DevOps Engineer at Amadeus Labs. He has over 7 years of experience in the IT industry, and is specialized in DevOps, GitOps, DevSecOps, MLOps, Chaos Engineering, Cloud and Cloud Native landscapes. He has published articles and blogs on recent tech trends and best practices on GitHub, Medium, and LinkedIn, and has delivered a DevSecOps 101 talk to Developers community , GitOps with Argo CD Webinar for DevOps Community. He has helped multiple enterprises with their cloud migration, cloud native design, CICD pipeline setup, and containerization journey.

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Cloud Computing – CLOUD 2021

14th International Conference, Held as Part of the Services Conference Federation, SCF 2021, Virtual Event, December 10–14, 2021, Proceedings

• Kejiang Ye 0 ,
• Liang-Jie Zhang   ORCID: https://orcid.org/0000-0002-6219-0853 1

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12989)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

Conference series link(s): CLOUD: International Conference on Cloud Computing

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Table of contents (7 papers)

Front matter, a brokering model for the cloud market.

• Georgios Chatzithanasis, Evangelia Filiopoulou, Christos Michalakelis, Mara Nikolaidou

An Experimental Analysis of Function Performance with Resource Allocation on Serverless Platform

• Yonghe Zhang, Kejiang Ye, Cheng-Zhong Xu

Electronic Card Localization Algorithm Based on Visible Light Screen Communication

• Kao Wen, Junjian Huang, Chan Zhou, Kejiang Ye

BBServerless: A Bursty Traffic Benchmark for Serverless

• Yanying Lin, Kejiang Ye, Yongkang Li, Peng Lin, Yingfei Tang, Chengzhong Xu

Performance Evaluation of Various RISC Processor Systems: A Case Study on ARM, MIPS and RISC-V

• Yu Liu, Kejiang Ye, Cheng-Zhong Xu

Comparative Analysis of Cloud Storage Options for Diverse Application Requirements

• Antara Debnath Antu, Anup Kumar, Robert Kelley, Bin Xie

COS2: Detecting Large-Scale COVID-19 Misinformation in Social Networks

• Hailu Xu, Macro Curci, Sophanna Ek, Pinchao Liu, Zhengxiong Li, Shuai Xu

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The 6 full papers and 1 short paper presented were carefully reviewed and selected from 25 submissions. They deal with the latest fundamental advances in the state of the art and practice of cloud computing, identify emerging research topics, and define the future of cloud computing.

• cloud computing
• Cloud Computing
• communication systems
• computer networks
• Distributed Architecture
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Liang-Jie Zhang

Book Title : Cloud Computing – CLOUD 2021

Book Subtitle : 14th International Conference, Held as Part of the Services Conference Federation, SCF 2021, Virtual Event, December 10–14, 2021, Proceedings

Editors : Kejiang Ye, Liang-Jie Zhang

Series Title : Lecture Notes in Computer Science

DOI : https://doi.org/10.1007/978-3-030-96326-2

Publisher : Springer Cham

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : Springer Nature Switzerland AG 2022

Softcover ISBN : 978-3-030-96325-5 Published: 26 February 2022

eBook ISBN : 978-3-030-96326-2 Published: 25 February 2022

Series ISSN : 0302-9743

Series E-ISSN : 1611-3349

Edition Number : 1

Number of Pages : XIII, 105

Number of Illustrations : 13 b/w illustrations, 35 illustrations in colour

Topics : Computer Communication Networks , Security , Database Management , Information Systems Applications (incl. Internet) , Software Engineering/Programming and Operating Systems

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Published: 14 February 2024 Contributors: Stephanie Susnjara, Ian Smalley

Cloud computing is the on-demand access of computing resources—physical servers or virtual servers, data storage, networking capabilities, application development tools, software, AI-powered analytic tools and more—over the internet with pay-per-use pricing.

The cloud computing model offers customers greater flexibility and scalability compared to traditional on-premises infrastructure.

Cloud computing plays a pivotal role in our everyday lives, whether accessing a cloud application like Google Gmail, streaming a movie on Netflix or playing a cloud-hosted video game.

Cloud computing has also become indispensable in business settings, from small startups to global enterprises. Its many business applications include enabling remote work by making data and applications accessible from anywhere, creating the framework for seamless omnichannel customer engagement and providing the vast computing power and other resources needed to take advantage of cutting-edge technologies like generative AI and quantum computing . 

A cloud services provider (CSP) manages cloud-based technology services hosted at a remote data center and typically makes these resources available for a pay-as-you-go or monthly subscription fee.

Read how Desktop as a service (DaaS) enables enterprises to achieve the same level of performance and security as deploying the applications on-premises.

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Compared to traditional on-premises IT that involves a company owning and maintaining physical data centers and servers to access computing power, data storage and other resources (and depending on the cloud services you select), cloud computing offers many benefits, including the following:

Cloud computing lets you offload some or all of the expense and effort of purchasing, installing, configuring and managing mainframe computers and other on-premises infrastructure. You pay only for cloud-based infrastructure and other computing resources as you use them. 

With cloud computing, your organization can use enterprise applications in minutes instead of waiting weeks or months for IT to respond to a request, purchase and configure supporting hardware and install software. This feature empowers users—specifically DevOps and other development teams—to help leverage cloud-based software and support infrastructure.

Cloud computing provides elasticity and self-service provisioning, so instead of purchasing excess capacity that sits unused during slow periods, you can scale capacity up and down in response to spikes and dips in traffic. You can also use your cloud provider’s global network to spread your applications closer to users worldwide.

Cloud computing enables organizations to use various technologies and the most up-to-date innovations to gain a competitive edge. For instance, in retail, banking and other customer-facing industries, generative AI-powered virtual assistants deployed over the cloud can deliver better customer response time and free up teams to focus on higher-level work. In manufacturing, teams can collaborate and use cloud-based software to monitor real-time data across logistics and supply chain processes.

The origins of cloud computing technology go back to the early 1960s when  Dr. Joseph Carl Robnett Licklider  (link resides outside ibm.com), an American computer scientist and psychologist known as the "father of cloud computing", introduced the earliest ideas of global networking in a series of memos discussing an Intergalactic Computer Network. However, it wasn’t until the early 2000s that modern cloud infrastructure for business emerged.

In 2002, Amazon Web Services started cloud-based storage and computing services. In 2006, it introduced Elastic Compute Cloud (EC2), an offering that allowed users to rent virtual computers to run their applications. That same year, Google introduced the Google Apps suite (now called Google Workspace), a collection of SaaS productivity applications. In 2009, Microsoft started its first SaaS application, Microsoft Office 2011. Today,  Gartner predicts  worldwide end-user spending on the public cloud will total USD 679 billion and is projected to exceed USD 1 trillion in 2027 (link resides outside ibm.com).

The following are a few of the most integral components of today’s modern cloud computing architecture.

CSPs own and operate remote data centers that house physical or bare metal servers , cloud storage systems and other physical hardware that create the underlying infrastructure and provide the physical foundation for cloud computing.

In cloud computing, high-speed networking connections are crucial. Typically, an internet connection known as a wide-area network (WAN) connects front-end users (for example, client-side interface made visible through web-enabled devices) with back-end functions (for example, data centers and cloud-based applications and services). Other advanced cloud computing networking technologies, including load balancers , content delivery networks (CDNs) and software-defined networking (SDN) , are also incorporated to ensure data flows quickly, easily and securely between front-end users and back-end resources. 

Cloud computing relies heavily on the virtualization of IT infrastructure —servers, operating system software, networking and other infrastructure that’s abstracted using special software so that it can be pooled and divided irrespective of physical hardware boundaries. For example, a single hardware server can be divided into multiple virtual servers . Virtualization enables cloud providers to make maximum use of their data center resources. 

IaaS (Infrastructure-as-a-Service), PaaS (Platform-as-a-Service), SaaS (Software-as-a-Service) and serverless computing are the most common models of cloud services, and it’s not uncommon for an organization to use some combination of all four.

IaaS (Infrastructure-as-a-Service) provides on-demand access to fundamental computing resources—physical and virtual servers, networking and storage—over the internet on a pay-as-you-go basis. IaaS enables end users to scale and shrink resources on an as-needed basis, reducing the need for high up-front capital expenditures or unnecessary on-premises or "owned" infrastructure and for overbuying resources to accommodate periodic spikes in usage. 

According to a  Business Research Company report  (link resides outside ibm.com), the IaaS market is predicted to grow rapidly in the next few years, growing to $212.34 billion in 2028 at a compound annual growth rate (CAGR) of 14.2%. 

PaaS (Platform-as-a-Service) provides software developers with an on-demand platform—hardware, complete software stack, infrastructure and development tools—for running, developing and managing applications without the cost, complexity and inflexibility of maintaining that platform on-premises. With PaaS, the cloud provider hosts everything at their data center. These include servers, networks, storage, operating system software, middleware  and databases. Developers simply pick from a menu to spin up servers and environments they need to run, build, test, deploy, maintain, update and scale applications.

Today, PaaS is typically built around  container s , a virtualized compute model one step removed from virtual servers. Containers virtualize the operating system, enabling developers to package the application with only the operating system services it needs to run on any platform without modification and the need for middleware.

Red Hat® OpenShift ® is a popular PaaS built around  Docker  containers and  Kubernetes , an open source container orchestration solution that automates deployment, scaling, load balancing and more for container-based applications.

SaaS (Software-as-a-Service) , also known as cloud-based software or cloud applications, is application software hosted in the cloud. Users access SaaS through a web browser, a dedicated desktop client or an API that integrates with a desktop or mobile operating system. Cloud service providers offer SaaS based on a monthly or annual subscription fee. They may also provide these services through pay-per-usage pricing. 

In addition to the cost savings, time-to-value and scalability benefits of cloud, SaaS offers the following:

• Automatic upgrades:  With SaaS, users use new features when the cloud service provider adds them without orchestrating an on-premises upgrade.
• Protection from data loss:  Because SaaS stores application data in the cloud with the application, users don’t lose data if their device crashes or breaks.

SaaS is the primary delivery model for most commercial software today. Hundreds of SaaS solutions exist, from focused industry and broad administrative (for example, Salesforce) to robust enterprise database and artificial intelligence (AI) software. According to an International Data Center (IDC) survey (the link resides outside IBM), SaaS applications represent the largest cloud computing segment, accounting for more than 48% of the $778 billion worldwide cloud software revenue.

Serverless computing , or simply serverless, is a cloud computing model that offloads all the back-end infrastructure management tasks, including provisioning, scaling, scheduling and patching to the cloud provider. This frees developers to focus all their time and effort on the code and business logic specific to their applications.

Moreover, serverless runs application code on a per-request basis only and automatically scales the supporting infrastructure up and down in response to the number of requests. With serverless, customers pay only for the resources used when the application runs; they never pay for idle capacity. 

FaaS, or Function-as-a-Service , is often confused with serverless computing when, in fact, it’s a subset of serverless. FaaS allows developers to run portions of application code (called functions) in response to specific events. Everything besides the code—physical hardware, virtual machine (VM) operating system and web server software management—is provisioned automatically by the cloud service provider in real-time as the code runs and is spun back down once the execution is complete. Billing starts when execution starts and stops when execution stops.

A  public cloud is a type of cloud computing in which a cloud service provider makes computing resources available to users over the public internet. These include SaaS applications, individual  virtual machines (VMs) , bare metal computing hardware, complete enterprise-grade infrastructures and development platforms. These resources might be accessible for free or according to subscription-based or pay-per-usage pricing models.

The public cloud provider owns, manages and assumes all responsibility for the data centers, hardware and infrastructure on which its customers’ workloads run. It typically provides high-bandwidth network connectivity to ensure high performance and rapid access to applications and data.

Public cloud is a  multi-tenant environment  where all customers pool and share the cloud provider’s data center infrastructure and other resources. In the world of the leading public cloud vendors, such as Amazon Web Services (AWS), Google Cloud, IBM Cloud®, Microsoft Azure and Oracle Cloud, these customers can number in the millions.

Most enterprises have moved portions of their computing infrastructure to the public cloud since public cloud services are elastic and readily scalable, flexibly adjusting to meet changing workload demands. The promise of greater efficiency and cost savings through paying only for what they use attracts customers to the public cloud. Still, others seek to reduce spending on hardware and on-premises infrastructure.  Gartner predicts  (link resides outside ibm.com) that by 2026, 75% of organizations will adopt a digital transformation model predicated on cloud as the fundamental underlying platform. 

A  private cloud is a cloud environment where all cloud infrastructure and computing resources are dedicated to one customer only. Private cloud combines many benefits of cloud computing—including elasticity, scalability and ease of service delivery—with the access control, security and resource customization of on-premises infrastructure.

A private cloud is typically hosted on-premises in the customer’s data center. However, it can also be hosted on an independent cloud provider’s infrastructure or built on rented infrastructure housed in an offsite data center.

Many companies choose a private cloud over a public cloud environment to meet their regulatory compliance requirements. Entities like government agencies, healthcare organizations and financial institutions often opt for private cloud settings for workloads that deal with confidential documents, personally identifiable information (PII), intellectual property, medical records, financial data or other sensitive data.

By building private cloud architecture according to  cloud-native  principles, an organization can quickly move workloads to a public cloud or run them within a hybrid cloud (see below) environment whenever ready.

A  hybrid cloud is just what it sounds like: a combination of public cloud, private cloud and on-premises environments. Specifically (and ideally), a hybrid cloud connects a combination of these three environments into a single, flexible infrastructure for running the organization’s applications and workloads. 

At first, organizations turned to hybrid cloud computing models primarily to migrate portions of their on-premises data into private cloud infrastructure and then connect that infrastructure to public cloud infrastructure hosted off-premises by cloud vendors. This process was done through a packaged hybrid cloud solution like Red Hat® OpenShift® or middleware and IT management tools to create a " single pane of glass ." Teams and administrators rely on this unified dashboard to view their applications, networks and systems.

Today, hybrid cloud architecture has expanded beyond physical connectivity and cloud migration to offer a flexible, secure and cost-effective environment that supports the portability and automated deployment of workloads across multiple environments. This feature enables an organization to meet its technical and business objectives more effectively and cost-efficiently than with a public or private cloud alone. For instance, a hybrid cloud environment is ideal for DevOps and other teams to develop and test web applications. This frees organizations from purchasing and expanding the on-premises physical hardware needed to run application testing, offering faster time to market. Once a team has developed an application in the public cloud, they may move it to a private cloud environment based on business needs or security factors.

A public cloud also allows companies to quickly scale resources in response to unplanned spikes in traffic without impacting private cloud workloads, a feature known as cloud bursting. Streaming channels like Amazon use cloud bursting to support the increased viewership traffic when they start new shows.

Most enterprise organizations today rely on a hybrid cloud model because it offers greater flexibility, scalability and cost optimization than traditional on-premises infrastructure setups. According to the  IBM Transformation Index: State of Cloud , more than 77% of businesses and IT professionals have adopted a hybrid cloud approach.

To learn more about the differences between public, private and hybrid cloud, check out “ Public cloud vs. private cloud vs. hybrid cloud: What’s the difference? ”

Watch the IBM hybrid cloud architecture video series.

Multicloud uses two or more clouds from two or more different cloud providers. A multicloud environment can be as simple as email SaaS from one vendor and image editing SaaS from another. But when enterprises talk about multicloud, they typically refer to using multiple cloud services—including SaaS, PaaS and IaaS services—from two or more leading public cloud providers. 

Organizations choose multicloud to avoid vendor lock-in, to have more services to select from and to access more innovation. With multicloud, organizations can choose and customize a unique set of cloud features and services to meet their business needs. This freedom of choice includes selecting “best-of-breed” technologies from any CSP, as needed or as they emerge, rather than being locked into offering from a single vendor. For example, an organization may choose AWS for its global reach with web-hosting, IBM Cloud for data analytics and machine learning platforms and Microsoft Azure for its security features.

A multicloud environment also reduces exposure to licensing, security and compatibility issues that can result from " shadow IT "— any software, hardware or IT resource used on an enterprise network without the IT department’s approval and often without IT’s knowledge or oversight.

Today, most enterprise organizations use a hybrid multicloud model. Apart from the flexibility to choose the most cost-effective cloud service, hybrid multicloud offers the most control over workload deployment, enabling organizations to operate more efficiently, improve performance and optimize costs. According to an  IBM® Institute for Business Value study , the value derived from a full hybrid multicloud platform technology and operating model at scale is two-and-a-half times the value derived from a single-platform, single-cloud vendor approach. 

Yet the modern hybrid multicloud model comes with more complexity. The more clouds you use—each with its own management tools, data transmission rates and security protocols—the more difficult it can be to manage your environment. With  over 97% of enterprises operating on more than one cloud  and most organizations running  10 or more clouds , a hybrid cloud management approach has become crucial. Hybrid multicloud management platforms provide visibility across multiple provider clouds through a central dashboard where development teams can see their projects and deployments, operations teams can monitor clusters and nodes and the cybersecurity staff can monitor for threats.

Learn more about hybrid cloud management.

Traditionally, security concerns have been the primary obstacle for organizations considering cloud services, mainly public cloud services. Maintaining cloud security demands different procedures and employee skillsets than in legacy IT environments. Some cloud security best practices include the following:

• Shared responsibility for security:  Generally, the cloud service provider is responsible for securing cloud infrastructure, and the customer is responsible for protecting its data within the cloud. However, it’s also essential to clearly define data ownership between private and public third parties.
• Data encryption:  Data should be encrypted while at rest, in transit and in use. Customers need to maintain complete control over security keys and hardware security modules.
• Collaborative management:  Proper communication and clear, understandable processes between IT, operations and security teams will ensure seamless cloud integrations that are secure and sustainable.
• Security and compliance monitoring:  This begins with understanding all regulatory compliance standards applicable to your industry and establishing active monitoring of all connected systems and cloud-based services to maintain visibility of all data exchanges across all environments, on-premises, private cloud, hybrid cloud and edge.

Cloud security is constantly changing to keep pace with new threats. Today’s CSPs offer a wide array of cloud security management tools, including the following:  

• Identity and access management (IAM):  IAM   tools and services that automate policy-driven enforcement protocols for all users attempting to access both on-premises and cloud-based services. 
• Data loss prevention (DLP): DLP services that combine remediation alerts data encryption and other preventive measures to protect all stored data, whether at rest or in motion.
• Security information and event management (SIEM) :   SIEM is a comprehensive security orchestration solution that automates threat monitoring, detection and response in cloud-based environments. SIEM technology uses artificial intelligence (AI)-driven technologies to correlate log data across multiple platforms and digital assets. This allows IT teams to successfully apply their network security protocols, enabling them to react to potential threats quickly.
• Automated data compliance platforms:   Automated software solutions provide compliance controls and centralized data collection to help organizations adhere to regulations specific to their industry. Regular compliance updates can be baked into these platforms so organizations can adapt to ever-changing regulatory compliance standards.

Learn more about cloud security.

Sustainability in business , a company’s strategy to reduce negative environmental impact from their operations in a particular market, has become an essential corporate governance mandate.  Moreover, Gartner predicts  (link resides outside ibm.com) that by 2025, the carbon emissions of hyperscale cloud services will be a top-three criterion in cloud purchase decisions.

As companies strive to advance their sustainability objectives, cloud computing has evolved to play a significant role in helping them reduce their carbon emissions and manage climate-related risks. For instance, traditional data centers require power supplies and cooling systems, which depend on large amounts of electrical power. By migrating IT resources and applications to the cloud, organizations only enhance operational and cost efficiencies and boost overall energy efficiency through pooled CSP resources.

All major cloud players have made net-zero commitments to reduce their carbon footprints and help clients reduce the energy they typically consume using an on-premises setup. For instance, IBM is driven by  sustainable procurement  initiatives to reach NetZero by 2030. By 2025, IBM Cloud worldwide data centers  will comprise energy procurement drawn from 75% renewable sources .

According to an  International Data Corporation (IDC) forecast  (link resides outside ibm.com), worldwide spending on the whole cloud opportunity (offerings, infrastructure and services) will surpass USD 1 trillion in 2024 while sustaining a double-digit compound annual growth rate (CAGR) of 15.7%. Here are some of the main ways businesses are benefitting from cloud computing: 

• Scale infrastructure:  Allocate resources up or down quickly and easily in response to changes in business demands.
• Enable business continuity and disaster recovery:  Cloud computing provides cost-effective redundancy to protect data against system failures and the physical distance required to apply disaster recovery strategies and recover data and applications during a local outage or disaster. All of the major public cloud providers offer Disaster-Recovery-as-a-Service (DRaaS) .
• Build and test cloud-native applications : For development teams adopting Agile,  DevOps  or  DevSecOps to streamline development, the cloud offers on-demand end-user self-service that prevents operations tasks, such as spinning up development and test servers, from becoming development bottlenecks.
• Support edge and IoT environments:  Address latency challenges and reduce downtime by bringing data sources closer to the edge . Support Internet of Things (IoT) devices (for example, patient monitoring devices and sensors on a production line) to gather real-time data.
• Leverage cutting-edge technologies:  Cloud computing supports storing and processing huge volumes of data at high speeds—much more storage and computing capacity than most organizations can or want to purchase and deploy on-premises. These high-performance resources support technologies like  blockchain , quantum computing and  large language models (LLMs ) that power generative AI platforms like customer service automation. 

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An industry-specific cloud, built to support your unique modernization and AI transformation needs.

Hybrid cloud integrates public cloud services, private cloud services and on-premises infrastructure into a single distributed computing environment.

DevOps speeds delivery of higher quality software by combining and automating the work of software development and IT operations teams.

Cloud migration is the process of relocating an organization’s data, applications, and workloads to a cloud infrastructure.

Although cloud computing is only a different way to deliver computer resources rather than a new technology, it has sparked a revolution in the way organizations provide information and service.

Determining the best cloud computing architecture for enterprise business is critical for overall success. That’s why it is essential to compare the different functionalities of private cloud versus public cloud versus hybrid cloud.

We're excited to introduce a three-part lightboarding video series that will delve into the world of hybrid cloud architecture. In this intro video, our guide, Sai Vennam, lays out the three major hybrid cloud architecture issues that we're going to cover: Connectivity, Modernization and Security.

Designed for industry, security and the freedom to build and run anywhere, IBM Cloud is a full stack cloud platform with over 170 products and services covering data, containers, AI, IoT and blockchain. Use IBM Cloud to build scalable infrastructure at a lower cost, deploy new applications instantly and scale up workloads based on demand.

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How the Cloud Is Changing Data Science

Here’s what leaders need to know in this shifting landscape.

Cloud tools and technologies are influencing the future of data science work in two key areas: scaling resources and improving workforce agility. If organizations want to make use of these capabilities, though, they also need to develop strong data security and privacy frameworks when operating in a cloud environment. The author shares some examples of how organizations are doing this work.

The role of “ data scientist ” is among the top 20 fastest-growing occupations in the U.S., with 36% projected growth by 2031. These data professionals fuel the growth and innovation that’s needed to solve some of the world’s biggest problems, from climate change to chronic disease. Yet common infrastructure issues, such as critical data being unavailable or unsecured, often stall an organization’s ability to innovate.

Peter Wang is the CEO and co-founder of  Anaconda , an operating system for AI.

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Breakthrough in Quantum Cloud Computing Ensures its Security and Privacy

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Businesses are one step closer to quantum cloud computing, thanks to a breakthrough made in its security and privacy by scientists at Oxford University.

The researchers used an approach dubbed ‘blind quantum computing’ to connect two quantum computing entities ( Figure A ); this simulates the situation where an employee at home or in an office remotely connects to a quantum server via the cloud. With this method, the quantum server provider does not need to know any details of the computation for it to be carried out, keeping the user’s proprietary work secure. The user can also easily verify the authenticity of their result, confirming it is neither erroneous nor corrupted.

Ensuring the security and privacy of quantum computations is one of the most significant roadblocks that has held the powerful technology back so far, so this work could lead to it finally entering the mainstream.

Despite only being tested on a small scale, the researchers say their experiment has the potential to be scaled up to large quantum computations. Plug-in devices could be developed that safeguard a worker’s data while they access quantum cloud computing services.

Professor David Lucas, the co-head of the Oxford University Physics research team, said in a press release : “We have shown for the first time that quantum computing in the cloud can be accessed in a scalable, practical way which will also give people complete security and privacy of data, plus the ability to verify its authenticity.”

What is quantum cloud computing?

Classical computers process information as binary bits represented as 1s and 0s, but quantum computers do so using quantum bits, or qubits. Qubits exist as both a 1 and a 0 at the same time, but with a probability of being one or the other that is determined by their quantum state. This property enables quantum computers to tackle certain calculations much faster than classical computers, as they can solve problems simultaneously.

Quantum cloud computing is where quantum resources are provided to users remotely over the internet; this allows anyone to utilise quantum computing without the need for specialised hardware or expertise.

FREE DOWNLOAD: Quantum computing: An insider’s guide

Why is ‘blind quantum computing’ more secure?

With typical quantum cloud computing, the user must divulge the problem they are trying to solve to the cloud provider; this is because the provider’s infrastructure needs to understand the specifics of the problem so it can allocate the appropriate resources and execution parameters. Naturally, in the case of proprietary work, this presents a security concern.

This security risk is minimised with the blind quantum computing method because the user remotely controls the quantum processor of the server themselves during a computation. The information required to keep the data secure — like the input, output and algorithmic details — only needs to be known by the client because the server does not make any decisions with it.

“Never in history have the issues surrounding privacy of data and code been more urgently debated than in the present era of cloud computing and artificial intelligence,” said Professor Lucas in the press release.

“As quantum computers become more capable, people will seek to use them with complete security and privacy over networks, and our new results mark a step change in capability in this respect.”

How could quantum computing impact business?

Quantum computing is vastly more powerful than conventional computing, and could revolutionise how we work if it is successfully scaled out of the research phase. Examples include solving supply chain problems , optimising routes and securing communications .

In February, the U.K. government announced a £45 million ($57 million) investment into quantum computing ; the money goes toward finding practical uses for quantum computing and creating a “quantum-enabled economy” by 2033. In March, quantum computing was singled out in the Ministerial Declaration , with G7 countries agreeing to work together to promote the development of quantum technologies and foster collaboration between academia and industry. Just this month, the U.K.’s second commercial quantum computer came online .

Due to the extensive power and refrigeration requirements, very few quantum computers are currently commercially available. However, several leading cloud providers do offer so-called quantum-as-a-service to corporate clients and researchers. Google’s Cirq, for example, is an open source quantum computing platform, while Amazon Braket allows users to test their algorithms on a local quantum simulator. IBM, Microsoft and Alibaba also have quantum-as-a-service offerings.

WATCH: What classic software developers need to know about quantum computing

But before quantum computing can be scaled up and used for business applications, it is imperative to ensure it can be achieved while safeguarding the privacy and security of customer data. This is what the Oxford University researchers hoped to achieve in their new study, published in Physical Review Letters .

Dr. Peter Dmota, study lead, told TechRepublic in an email: “Strong security guarantees will lower the barrier to using powerful quantum cloud computing services, once available, to speed up the development of new technologies, such as batteries and drugs, and for applications that involve highly confidential data, such as private medical information, intellectual property, and defence. Those applications exist also without added security, but would be less likely to be used as widely.

“Quantum computing has the potential to drastically improve machine learning. This would supercharge the development of better and more adapted artificial intelligence, which we are already seeing impacting businesses across all sectors.

“It is conceivable that quantum computing will have an impact on our lives in the next five to ten years, but it is difficult to forecast the exact nature of the innovations to come. I expect a continuous adaptation process as users start to learn how to use this new tool and how to apply it to their jobs — similar to how AI is slowly becoming more relevant at the mainstream workplace right now.

“Our research is currently driven by quite general assumptions, but as businesses start to explore the potential of quantum computing for them, more specific requirements will emerge and drive research into new directions.”

How does blind quantum cloud computing work?

Blind quantum cloud computing requires connecting a client computer that can detect photons, or particles of light, to a quantum computing server with a fibre optic cable ( Figure B ). The server generates single photons, which are sent through the fibre network and received by the client.

The client then measures the polarisation, or orientation, of the photons, which tells it how to remotely manipulate the server in a way that will produce the desired computation. This can be done without the server needing access to any information about the computation, making it secure.

To provide additional assurance that the results of the computation are not erroneous or have been tampered with, additional tests can be undertaken. While tampering would not harm the security of the data in a blind quantum computation, it could still corrupt the result and leave the client unaware.

“The laws of quantum mechanics don’t allow copying of information and any attempt to observe the state of the memory by the server or an eavesdropper would corrupt the computation,” Dr Dmota explained to TechRepublic in an email. “In that case, the user would notice that the server isn’t operating faithfully, using a feature called ‘verification’, and abort using their service if there are any doubts.

“Since the server is ‘blind’ to the computation — ie, is not able to distinguish different computations — the client can evaluate the reliability of the server by running simple tests whose results can be easily checked.

“These tests can be interleaved with the actual computation until there is enough evidence that the server is operating correctly and the results of the actual computation can be trusted to be correct. This way, honest errors as well as malicious attempts to tamper with the computation can be detected by the client.”

What did the researchers discover through their blind quantum cloud computing experiment?

The researchers found the computations their method produced “could be verified robustly and reliably”, as per the paper. This means that the client can trust the results have not been tampered with. It is also scalable, as the number of quantum elements being manipulated for performing calculations can be increased “without increasing the number of physical qubits in the server and without modifications to the client hardware,” the scientists wrote.

Dr. Drmota said in the press release, “Using blind quantum computing, clients can access remote quantum computers to process confidential data with secret algorithms and even verify the results are correct, without revealing any useful information. Realising this concept is a big step forward in both quantum computing and keeping our information safe online.”

The research was funded by the UK Quantum Computing and Simulation Hub — a collaboration of 17 universities supported by commercial and government organisations. It is one of four quantum technology hubs in the UK National Quantum Technologies Programme.

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Breakthrough promises secure quantum computing at home

The full power of next-generation quantum computing could soon be harnessed by millions of individuals and companies, thanks to a breakthrough by scientists at Oxford University Physics guaranteeing security and privacy. This advance promises to unlock the transformative potential of cloud-based quantum computing and is detailed in a new study published in the influential U.S. scientific journal Physical Review Letters .

Quantum computing is developing rapidly, paving the way for new applications which could transform services in many areas like healthcare and financial services. It works in a fundamentally different way to conventional computing and is potentially far more powerful. However, it currently requires controlled conditions to remain stable and there are concerns around data authenticity and the effectiveness of current security and encryption systems.

Several leading providers of cloud-based services, like Google, Amazon, and IBM, already separately offer some elements of quantum computing. Safeguarding the privacy and security of customer data is a vital precursor to scaling up and expending its use, and for the development of new applications as the technology advances. The new study by researchers at Oxford University Physics addresses these challenges.

"We have shown for the first time that quantum computing in the cloud can be accessed in a scalable, practical way which will also give people complete security and privacy of data, plus the ability to verify its authenticity," said Professor David Lucas, who co-heads the Oxford University Physics research team and is lead scientist at the UK Quantum Computing and Simulation Hub, led from Oxford University Physics.

In the new study, the researchers use an approach dubbed "blind quantum computing," which connects two totally separate quantum computing entities -- potentially an individual at home or in an office accessing a cloud server -- in a completely secure way. Importantly, their new methods could be scaled up to large quantum computations.

"Using blind quantum computing, clients can access remote quantum computers to process confidential data with secret algorithms and even verify the results are correct, without revealing any useful information. Realising this concept is a big step forward in both quantum computing and keeping our information safe online'' said study lead Dr Peter Drmota, of Oxford University Physics.

The researchers created a system comprising a fibre network link between a quantum computing server and a simple device detecting photons, or particles of light, at an independent computer remotely accessing its cloud services. This allows so-called blind quantum computing over a network. Every computation incurs a correction which must be applied to all that follow and needs real-time information to comply with the algorithm. The researchers used a unique combination of quantum memory and photons to achieve this.

"Never in history have the issues surrounding privacy of data and code been more urgently debated than in the present era of cloud computing and artificial intelligence," said Professor David Lucas. "As quantum computers become more capable, people will seek to use them with complete security and privacy over networks, and our new results mark a step change in capability in this respect."

The results could ultimately lead to commercial development of devices to plug into laptops, to safeguard data when people are using quantum cloud computing services.

Researchers exploring quantum computing and technologies at Oxford University Physics have access to the state-of-the-art Beecroft laboratory facility, specially constructed to create stable and secure conditions including eliminating vibration.

Funding for the research came from the UK Quantum Computing and Simulation (QCS) Hub, with scientists from the UK National Quantum Computing Centre, the Paris-Sorbonne University, the University of Edinburgh, and the University of Maryland, collaborating on the work.

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Materials provided by University of Oxford . Note: Content may be edited for style and length.

Journal Reference :

• P. Drmota, D. P. Nadlinger, D. Main, B. C. Nichol, E. M. Ainley, D. Leichtle, A. Mantri, E. Kashefi, R. Srinivas, G. Araneda, C. J. Ballance, D. M. Lucas. Verifiable Blind Quantum Computing with Trapped Ions and Single Photons . Physical Review Letters , 2024; 132 (15) DOI: 10.1103/PhysRevLett.132.150604

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Meeting Time: 09:45 AM‑11:00 AM TTh  Instructor: Ali Anwar Course Description: Cloud computing serves many large-scale applications ranging from search engines like Google to social networking websites like Facebook to online stores like Amazon. More recently, cloud computing has emerged as an essential technology to enable emerging fields such as Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning. The exponential growth of data availability and demands for security and speed has made the cloud computing paradigm necessary for reliable, financially economical, and scalable computation. The dynamicity and flexibility of Cloud computing have opened up many new forms of deploying applications on infrastructure that cloud service providers offer, such as renting of computation resources and serverless computing.    This course will cover the fundamentals of cloud services management and cloud software development, including but not limited to design patterns, application programming interfaces, and underlying middleware technologies. More specifically, we will cover the topics of cloud computing service models, data centers resource management, task scheduling, resource virtualization, SLAs, cloud security, software defined networks and storage, cloud storage, and programming models. We will also discuss data center design and management strategies, which enable the economic and technological benefits of cloud computing. Lastly, we will study cloud storage concepts like data distribution, durability, consistency, and redundancy. Registration Prerequisites: CS upper div, CompE upper div., EE upper div., EE grad, ITI upper div., Univ. honors student, or dept. permission; no cr for grads in CSci. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/6BvbUwEkBK41tPJ17 ).

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Machine learning for healthcare: concepts and applications.

Meeting Time: 11:15 AM‑12:30 PM TTh  Instructor: Yogatheesan Varatharajah Course Description: Machine Learning is transforming healthcare. This course will introduce students to a range of healthcare problems that can be tackled using machine learning, different health data modalities, relevant machine learning paradigms, and the unique challenges presented by healthcare applications. Applications we will cover include risk stratification, disease progression modeling, precision medicine, diagnosis, prognosis, subtype discovery, and improving clinical workflows. We will also cover research topics such as explainability, causality, trust, robustness, and fairness.

Registration Prerequisites: CSCI 5521 or equivalent. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/z8X9pVZfCWMpQQ6o6  ).

Visualization with AI

Meeting Time: 04:00 PM‑05:15 PM TTh  Instructor: Qianwen Wang Course Description: This course aims to investigate how visualization techniques and AI technologies work together to enhance understanding, insights, or outcomes.

This is a seminar style course consisting of lectures, paper presentation, and interactive discussion of the selected papers. Students will also work on a group project where they propose a research idea, survey related studies, and present initial results.

This course will cover the application of visualization to better understand AI models and data, and the use of AI to improve visualization processes. Readings for the course cover papers from the top venues of AI, Visualization, and HCI, topics including AI explainability, reliability, and Human-AI collaboration.    This course is designed for PhD students, Masters students, and advanced undergraduates who want to dig into research.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/YTF5EZFUbQRJhHBYA  ). Although the class is primarily intended for PhD students, motivated juniors/seniors and MS students who are interested in this topic are welcome to apply, ensuring they detail their qualifications for the course.

Visualizations for Intelligent AR Systems

Meeting Time: 04:00 PM‑05:15 PM MW  Instructor: Zhu-Tian Chen Course Description: This course aims to explore the role of Data Visualization as a pivotal interface for enhancing human-data and human-AI interactions within Augmented Reality (AR) systems, thereby transforming a broad spectrum of activities in both professional and daily contexts. Structured as a seminar, the course consists of two main components: the theoretical and conceptual foundations delivered through lectures, paper readings, and discussions; and the hands-on experience gained through small assignments and group projects. This class is designed to be highly interactive, and AR devices will be provided to facilitate hands-on learning.    Participants will have the opportunity to experience AR systems, develop cutting-edge AR interfaces, explore AI integration, and apply human-centric design principles. The course is designed to advance students' technical skills in AR and AI, as well as their understanding of how these technologies can be leveraged to enrich human experiences across various domains. Students will be encouraged to create innovative projects with the potential for submission to research conferences.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/Y81FGaJivoqMQYtq5 ). Students are expected to have a solid foundation in either data visualization, computer graphics, computer vision, or HCI. Having expertise in all would be perfect! However, a robust interest and eagerness to delve into these subjects can be equally valuable, even though it means you need to learn some basic concepts independently.

Sustainable Computing: A Systems View

Meeting Time: 09:45 AM‑11:00 AM  Instructor: Abhishek Chandra Course Description: In recent years, there has been a dramatic increase in the pervasiveness, scale, and distribution of computing infrastructure: ranging from cloud, HPC systems, and data centers to edge computing and pervasive computing in the form of micro-data centers, mobile phones, sensors, and IoT devices embedded in the environment around us. The growing amount of computing, storage, and networking demand leads to increased energy usage, carbon emissions, and natural resource consumption. To reduce their environmental impact, there is a growing need to make computing systems sustainable. In this course, we will examine sustainable computing from a systems perspective. We will examine a number of questions:   • How can we design and build sustainable computing systems?   • How can we manage resources efficiently?   • What system software and algorithms can reduce computational needs?    Topics of interest would include:   • Sustainable system design and architectures   • Sustainability-aware systems software and management   • Sustainability in large-scale distributed computing (clouds, data centers, HPC)   • Sustainability in dispersed computing (edge, mobile computing, sensors/IoT)

Registration Prerequisites: This course is targeted towards students with a strong interest in computer systems (Operating Systems, Distributed Systems, Networking, Databases, etc.). Background in Operating Systems (Equivalent of CSCI 5103) and basic understanding of Computer Networking (Equivalent of CSCI 4211) is required.

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