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Top 10 Cloud Computing Research Topics of 2024

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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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Top 15 Cloud Computing Research Topics in 2024

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 , and 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 is at 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 of the most demanded research topics in the field of cloud computing .

What is Cloud Computing?

Cloud computing is the practice of storing and accessing data and applications on remote servers hosted over the internet, as opposed to local servers or the computer’s hard drive. Cloud computing, often known as Internet-based computing, is a technique in which the user receives a resource as a service via the Internet. Files, photos, documents, and other storable documents can all be considered types of data that are stored.

Let us look at the latest in cloud computing research for 2024! We’ve compiled 15 important cloud computing research topics that are changing how cloud computing is used.

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 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 within a few minutes and a single click. They can be customized as per the client’s 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 needs 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 encryption techniques or by using the concept of the private key . It can make the plain text unreadable and limit 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 server 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 data warehouse. 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 of 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 . The 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. A hybrid cloud is a mixture of private and public clouds with critical activities being performed using the private cloud and non-critical activities being performed using the public cloud. Community cloud allows systems and services to be accessible by a group of organizations.

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.

11. Serverless Computing

Serverless computing is a way of running computer programs without having to manage the underlying infrastructure. Instead of worrying about servers, networking, and scaling, you can focus solely on writing code to solve your problem. In serverless computing, you write small pieces of code called functions. These functions are designed to do specific tasks, like processing data, handling user requests, or performing calculations. When something triggers your function, like a user making a request to your website or a timer reaching a certain time, the cloud provider automatically runs your function for you. You don’t have to worry about setting up servers or managing resources.

12. Cloud-Native Applications

Modern applications built for the cloud , also known as cloud-native applications , are made so to take full advantage of cloud computing environments . Instead of bulky programs like monolithic systems , they’re built to prioritize flexibility , easy scaling , reliability , and constant updates . This modular approach allows them to adapt to changing needs by growing or shrinking on demand, making them perfect for the ever-shifting world of cloud environments. Deployed in various cloud environments like public, private, or hybrid clouds, they’re optimized to make the most of cloud-native technologies and methodologies . Instead of one big chunk, they’re made up of lots of smaller pieces called microservices .

13. Multi-Cloud Management

Multi-cloud management means handling and controlling your stuff (like software, data, and services) when they’re spread out across different cloud companies, like Amazon, Google, or Microsoft. It’s like having a central command center for your cloud resources spread out across different cloud services. Multi-cloud gives you the freedom to use the strengths of different cloud providers. You can choose the best service for each specific workload, based on factors like cost, performance, or features. This flexibility allows you to easily scale your applications up or down as required by you. Managing a complex environment with resources spread across multiple cloud providers can be a challenge. Multi-cloud management tools simplify this process by providing a unified view and standardized management interface.

14. Blockchain in Cloud Computing

Cloud computing provides flexible storage and processing power that can grow or shrink as needed. Blockchain keeps data secure by spreading it across many computers. When we use them together, blockchain apps can use the cloud’s power for big tasks while keeping data safe and transparent. This combo boosts cloud data security and makes it easy to track data. It also lets people manage their identities without a central authority. However, there are challenges like making sure different blockchain and cloud systems work well together and can handle large amounts of data.

15. Cloud-Based Internet of Things (IoT)

Cloud-based Internet of Things (IoT) refers to the integration of cloud computing with IoT devices and systems. This integration allows IoT devices to leverage the computational power, storage, and analytics capabilities of cloud platforms to manage, process, and analyze the vast amounts of data they generate. The cloud serves as a central hub for connecting and managing multiple IoT devices, regardless of their geographical location. This connectivity is crucial for monitoring and controlling devices remotely.

Also Read Cloud computing Research challenges 7 Privacy Challenges in Cloud Computing Difference Between Cloud Computing and Fog Computing

Cloud computing has helped businesses grow by offering greater scalability , flexibility , and saving money by charging less money for the same job. As cloud computing is having a great growth period right now, it has created lots of employment opportunities and research work is done is different areas which is changing the future of this technology. We have discussed about the top 15 cloud computing research topics . You can try to explore and research in these areas to contribute to the growth of cloud computing technology .

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12 Latest Cloud Computing Research Topics

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Cloud Computing is gaining so much popularity an demand in the market. It is getting implemented in many organizations very fast.

One of the major barriers for the cloud is real and perceived lack of security. There are many Cloud Computing Research Topics ,  which can be further taken to get the fruitful output.

In this tutorial, we are going to discuss 12 latest Cloud Computing Research Topics. These Cloud computing topics will help in your researches, projects and assignments.

So, let’s start the Cloud Computing Research Topics.

List of Cloud Computing Research Topics

These Cloud Computing researches topics, help you to can eliminate many issues and provide a better environment. We can assoicate these issues with:

• Virtualizations infrastructure
• Software platform
• Identity management
• Access control

There is some important research direction in Cloud Security in areas such as trusted computing, privacy-preserving models, and information-centric security. These are the following Trending Cloud Computing Research Topics .

• Green Cloud Computing
• Edge Computing
• Cloud Cryptography
• Load Balancing
• Cloud Analytics
• Cloud Scalability
• Service Model
• Cloud Computing Platforms
• Mobile Cloud Computing
• Cloud Deployment Model
• Cloud Security

i. Green Cloud Computing

Green Cloud Computing is a broad topic, that makes virtualized data centres and servers to save energy. The IT services are utilizing so many resources and this leads to the shortage of resources.

Green Cloud Computing provides many solutions, which makes IT resources more energy efficient and reduces the operational cost. It can also take care of power management, virtualization , sustainability, and recycling the environment.

ii. Edge Computing

Although edge computing has several benefits, it is frequently combined with cloud computing to form a hybrid strategy. In this hybrid architecture, certain data processing and analytics take place at the edge, while more intense and extensive long-term data storage and analysis happen in the central cloud infrastructure. The edge-to-cloud continuum refers to this fusion of edge and cloud computing.

iii. Cloud Cryptography

Cloud cryptography is the practise of securing data and communications in cloud computing environments using cryptographic methods and protocols. Sensitive data is secured against unauthorised access and possible security breaches by encrypting it both in transit and at rest.

By allowing consumers to keep control of their data while entrusting it to cloud service providers, cloud cryptography protects the confidentiality, integrity, and authenticity of that data. Cloud cryptography improves the security posture of cloud-based apps and services, promoting trust and compliance with data privacy rules by using encryption methods and key management procedures.

iv. Load Balancing

Load Balancing is the distribution of the load over the servers so that the work can be easily done. Due to this, the workload demands can be distributed and managed. There are several advantages of load balancing and they are-

• Fewer chances of the server crash.
• Advanced security.
• Improvement in overall performance.

The load balancing techniques are easy to implement and less expensive. Moreover, the problem of sudden outages is diminished.

v. Cloud Analytics

Cloud analytics can become an interesting topic for researchers, as it has evolved from the diffusion of data analytics and cloud computing technologies . The Cloud analytics is beneficial for small as well as large organizations.

It has been observed that there is tremendous growth in the cloud analytics market. Moreover, it can be delivered through various models such as

• Community model

Analysis has a wide scope, as there are many segments to perform research. Some of the segments are  business intelligence tools , enterprise information management, analytics solutions, governance, risk and compliance, enterprise performance management, and complex event processing

vi. Scalability

Scalability can reach much advancement if proper research is done on it. Many limits can be reached and tasks such as workload in infrastructure can be maintained. It also has the ability to expand the existing infrastructure.

There are two types of scalability:

The applications have rooms to scale up and down, which eliminates the lack of resources that hamper the performance.

vii. Cloud Computing Platforms

Cloud Computing platforms include different applications run by organizations. It is a very vast platform and we can do many types of research within it. We can do research in two ways: individually or in an existing platform, some are-

• Amazon’s Elastic Compute Cloud
• IBM Computing
• Microsoft’s Azure
• Google’s AppEngine
• Salesforce.com

viii. Cloud Service Model

There are 3 cloud service models. They are:

• Platform as a Service (PaaS)
• Software as a Service (SaaS)
• Infrastructure as a Service (IaaS)

These are the vast topics for research and development as IaaS provides resources such as storage , virtual machines, and network to the users. The user further deploys and run software and applications. In software as a service , the software services are delivered to the customer.

The customer can provide various software services and can do research on it. PaaS also provides the services over the internet such as infrastructure and the customers can deploy over the existing infrastructure.

ix. Mobile Cloud Computing

In mobile cloud computing , the mobile is the console and storage and processing of the data takes outside of it. It is one of the leading Cloud Computing research topics.

The main advantage of Mobile Cloud Computing is that there is no costly hardware and it comes with extended battery life. The only disadvantage is that has low bandwidth and heterogeneity.

x. Big Data

Big data is the technology denotes the tremendous amount of data. This data is classified in 2 forms that are structured (organized data) and unstructured (unorganized).

Big data is characterized by three Vs which are:

• Volume – It refers to the amount of data which handled by technologies such as Hadoop.
• Variety –  It refers to the present format of data.
• Velocity – It means the speed of data (generation and transmission).

This can be used for research purpose and companies can use it to detect failures, costs, and issues. Big data along with Hadoop is one of the major topics for research.

xi. Cloud Deployment Model

Deployment model is one of the major Cloud Computing research topics, which includes models such as:

Public Cloud –  It is under the control of the third party. It has a benefit of pay-as-you-go.

Private Cloud – It is under a single organization and so it has few restrictions. We can use it for only single or a particular group of the organization.

Hybrid Cloud – The hybrid cloud comprises of two or more different models. Its architecture is complex to deploy.

Community Cloud

x. Cloud Security

Cloud Security is one of the most significant shifts in information technology. Its development brings revolution to the current business model. There is an open Gate when cloud computing as cloud security is becoming a new hot topic.

To build a strong secure cloud storage model and Tekken issues faced by the cloud one can postulate that cloud groups can find the issues, create a context-specific access model which limits data and preserve privacy.

In security research, there are three specific areas such as trusted computing, information-centric security, and privacy-preserving models.

Cloud Security protects the data from leakage, theft, disaster, and deletion. With the help of tokenization, VPNs, and firewalls, we can secure our data. Cloud Security is a vast topic and we can use it for more researches.

The number of organizations using cloud services is increasing. There are some security measures, which will help to implement the cloud security-

• Accessibility
• Confidentiality

So, this was all about Cloud Computing Research Topics. Hope you liked our explanation.

Hence, we can use Cloud Computing for remote processing of the application, outsourcing, and data giving quick momentum. The above Cloud Computing research topics can help a lot to provide various benefits to the customer and to make the cloud better.

With these cloud computing research, we can make this security more advanced. There are many high-level steps towards security assessment framework. This will provide many benefits in the future to cloud computing. Furthermore, if you have any query, feel free to ask in the comment section.

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Tags: big data Cloud Analytics Cloud Computing Platforms cloud computing research Cloud Computing Research Topics Cloud Computing Topics Cloud Cryptography Cloud Deployment Model Cloud Scalability Cloud Security Cloud Service Model Edge Computing Green Cloud Computing Load Balancing Mobile Cloud Computing Research Topics on Cloud Computing

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Dear, I wants to write a research paper on the cloud computing security, will also discuss the comparison of the present security shecks vs improvement suggested, I am thankful to you, as your paper helps me…

hay thanks for this valueable information dear i am just going to start my research in cloud computing from scratch i dnt now more about this field but i have to now work hard for this so plz give me idea how i start with effiecient manner

Hey Yaseen, Research is a great way to explore the entire topic. But it is recommended you master Cloud computing first, then start your research. Refer to our Free Cloud Computing Tutorial Series You can research on topics like Cloud Security, Optimization of resources, and Cloud cryptography.

Hi, Thank you for your article. I’m working on Cloud Computing Platforms research paper. Would you recommend any sources where I can get a real data or DB with numbers on cloud computing platforms. So, I can analyze it, create graphs, and draw a conclusion. Thank you

….or any sources with data on Cloud Service Models. Thank you

Can you please provide your contact details as I am also starting to research on Cloud Computing, Am a 11 years exp Consultant in an MNC working in Large Infrastructure. My email is partha.059@gmail .com so that we can communicate accordingly.

Can you please put some references you used, so that we can refer for more information? Thanks.

Hi, Very much pleased to know the latest topic for research. very informative, thanks for this i am interested in optimizing the resource here when i say resource it becomes too vast in terms of cloud computing components according to the definition of cloud computing. bit confused to hit the link.. could you plz.

hello iam searching for research gap in cloud computing I cant identify the problem please suggest me research topic on cloud computing

hello I am searching for research gap in cloud computing I cant identify the problem please suggest me research topic on cloud computing

we discuss optimization of resources, the gaps available

I want to do research in cloud databases,may i know the latest challenges in cloud databases?

I am a student of MS(computer science) and i am currently finding research topics in the area of cloud computing, Please let me know the topic of cloud computing and as well research gap so i will continue the research ahead with research gap.

Hi I am a student of MS(computer science) and i am currently finding research topics in the area of cloud computing, Please let me know the topic of cloud computing and as well research gap so I will continue the research.

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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.

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PhD Research Topics in 5 Cool Cloud Computing

PhD research topics in 5 Cool Cloud Computing escalates you to the crown of the invention in your research. In fact, it holds your hand and leads you to the  “minute research”  in your study area. All in all, the  central applications are moving towards this arising technology  due to its inspiring growth.

Terrific Features of Cloud Computing

• Data Containers
• Hybrid-Multi Cloud
• Security Assisted Cloud
• Service Mesh Development
• Server-Less Computing

Edge Computing

• Platform for AI
• Open Source
• And also Disaster Rescue

By all means, we are skilled enough to work above all areas. And so,  PhD research topics in 5 Cool Cloud Computing  are all set to guide you also from  “TOP TO BOTTOM”  of your research trip. For the most part, it starts from “ topic selection”  and ends with “ project delivery.”  During this period, we also assist you in  any and all angles of your research .

Let’s have a look of happening PhD research topics in 5 Cool Cloud Computing

Cloud service model and analytics.

• Service Simulation
• Service Analytics

5G- Mobile Computing Security and Integrity

• Architecture
• Virtualization Security
• Digital Forensics and also so on

Dynamic Resource Provision

• VMs Selection
• PMs/VM Deployment
• Software and also Hardware Management

Scheduling for Optimal Resource Usage

• QoS-Driven Task Scheduling
• Hyper-Heuristic Scheduling
• Multi-Objective & also Task Scheduling

Integrated Cloud Computing

• Fog-Cloud Computing
• Edge-Cloud Computing
• And Mobile-Cloud Computing

To be sure, our research outcome will  “match up with your expected outcome”  in all aspects. As a matter of fact, we will also deliver our work on time in  “good quality.”

Turn OFF your research fears and Commit with us to Turn ON your SUCCESS!!!

On the whole, it is best to approach us in the earlier stage itself for assistance related to PhD research topics in 5 Cool Cloud Computing . In particular, it saves you  time and effort . It is also easy for us to direct you on the right path from the beginning to the end.

More research notions also are harvested from our research topics in 5 Cool Cloud Computing,

Integrating Fog-Cloud  Computing for Secure Data Storage and Searching in IIoT

Reducing Big Biometric Data in Cloud using Tensor

Distributed Mobile Cloud Computing Services using Privacy-Aware Authentication Scheme

Allocating Optimal and Fair Service in Mobile Cloud Computing

An Energy-Efficient Dynamic Computation Offloading with Cooperative Task Scheduling in MCC

Resource Sharing Computing Access Point for Delay Constrained Multi-User Mobile Cloud Offloading

A new multi-resource allocation mechanism for tradeoff between fairness and efficiency in cloud computing

Healthcare-Based Secure Fine-Grained Data Access Control in  Multi-Cloud Servers in Mobile Cloud Computing

MapReduce Scheduling with Deadline-Constrain Jobs in Heterogeneous Cloud Computing Systems

Multi-Attribute Trusted Cloud Service Selection using Normal Cloud Model-Based Algorithm

A Fog-Assisted Computational Intelligence using Three-Layer Privacy Preserving Cloud Storage Scheme

Energy Consumption  Minimization and SLA Violation using Adaptive Energy-Aware Algorithms in Cloud Computing

Distributed Blockchain Cloud Architecture using Software Defined Fog Node for IoT

Computation Offloading and Resource Allocation with Min-Max Fairness Guarantee in Fog-Cloud Computing

Heterogeneous Cloud Computing for Resource Management in Sustainable Cyber-Physical Systems

Collaborative Simulation Development  using Knowledge-Based Resource Allocation in a Multi-Tenant Cloud Computing Environment

Geo-Distributed Mobile Cloud Computing via Decentralized and Optimal Resource Cooperation

Distributed Resource Allocation and Computation Offloading with Non-Orthogonal Multiple Access in Fog-Cloud Networks

Deniable Attribute-Based Encryption in Audit-Free Cloud Storage

Estimating Source-Level Energy Consumption for Tasks in Cloud Computing

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PhD in Computing & Data Sciences

For more information and to get in touch, please visit the Faculty of Computing & Data Sciences website .

The PhD program in Computing & Data Sciences (CDS) at Boston University prepares its graduates to make significant contributions to the art, science, and engineering of computational and data-driven processes that are woven into all aspects of society, economy, and public discourse, leading to solutions of problems and synthesis of knowledge related to the methodical, generalizable, and scalable extraction of insights from data as well as the design of new information systems and products that enable actionable use of those insights to advance scholarly as well as practical pursuits in a wide range of application domains.

Applicants to the PhD program in CDS are expected to have earned a bachelor’s or master’s degree in one of the methodological or applied disciplines relating to the computational and data-driven areas of scholarship in CDS. They are expected to possess basic mathematical and computational competencies, and demonstrable propensity for cross-disciplinary work. To accommodate a diversity of student backgrounds and preparations, a holistic admission review is utilized. As such, GRE tests and scores are not required, but could be optionally provided and considered as part of the applicant’s portfolio, which may also include evidence of prior, relevant preparation, including creative works, software code repositories, etc. Special attention will be paid to applicants from underrepresented backgrounds in computing and data science disciplines.

Completion of the PhD degree in CDS requires coursework covering breadth and depth topics spanning the foundational, applied, and sociotechnical dimensions of computing and data science; completion of research rotations that expose students to ongoing projects; completion of a cohort-based training on ethical and responsible computing; and successful proposal and defense of a doctoral thesis.

For their thesis work, and in preparation for careers in academia, industry, and government, CDS PhD students are expected to pursue theoretical, applied, or empirical studies leading to solution of new problems and synthesis of new knowledge in a topic area determined in consultation with their mentors and collaborators, which may include external researchers and practitioners in industrial and academic research laboratories.

Upon completion of the program, students will be prepared to pursue careers in which they lead independent cutting-edge research and development agendas, whether in academia (by teaching, mentoring, and supervising teams of students engaged in scholarly pursuits) or in industry (by collaborating, directing, and effectively managing diverse teams of practitioners working at the forefront of industrial R&D).

Learning Outcomes

The following learning outcomes explain what you will be able to do at the end of your time as a CDS PhD candidate, as a result of earning your degree.

• Exhibit a strong grasp of the principles governing the design and implementation of the methodological approaches for computational and data-driven inquiry.
• Identify the literature and demonstrate mastery of the compendium of works relevant to a well-defined area of research inquiry in computing and data sciences.
• Show capacity to engage meaningfully in and materially contribute to multidisciplinary research and development endeavors.
• Evidence a strong sense of social and professional responsibility for decisions related to the development and deployment of computational and data-driven technologies.
• Assess and argue the merits, limitations, and possibilities of new research work in a specialized area at the level commensurate with standards of scholarly venues in that area.
• Formulate and pursue a research agenda leading to solution of new problems and to synthesis of new knowledge shared through peer-reviewed publications.

Course Requirements

Sixteen term courses (64 units) are required for post-BA/BS students and 12 term courses (48 units) are required for post-MA/MS students. Students with prior graduate work (including master’s degrees) may be able to transfer up to two courses (8 units) as long as these units were not used to fulfill matriculation requirements, upon the recommendation of the student’s academic advisor, and subject to approval by the Associate Provost for CDS.

Of the 16 courses, up to 3 undergraduate courses (12 units) may be counted as background courses, selected in consultation with the student’s academic advisor and subject to approval by the Associate Provost for CDS. Other than these remedial courses, all other courses must be graduate-level courses or directed studies offered by CDS or by other BU departments in order to satisfy the following degree requirements.

The methodology core requirement ensures that students possess foundational knowledge and competencies in a subset of the following eight methodological areas of CDS:

• Mathematical Foundations of Data Science
• Statistical Modeling and Inference
• Efficient and Scalable Algorithms
• Predictive Analytics and Machine Learning
• Combinatorial Optimization and Algorithms
• Computational Complexity
• Programming and Software Design
• Large-scale Data Management

A list of courses that can be used to satisfy these competencies will be maintained on the website for CDS. Students who start their PhD program in CDS are expected to satisfy at least six of these competencies. Students who complete the course requirement for the PhD program in a cognate discipline are expected to satisfy at least four of these competencies.

The subject core requirement ensures that students establish depth in one area of inquiry that is aligned with either the methodological or applied dimensions of CDS. Subject areas are defined by groups of CDS faculty members working in related disciplinary and/or interdisciplinary areas of research who expect their prospective students to have enough depth in the subset of topics to enable them to tackle doctoral-level research in these topics. The set of subject areas as well as a list of preapproved graduate-level courses offered in CDS or elsewhere at BU that can be used to satisfy each subject area will be maintained on the website for CDS.

During the first two years in the program, all PhD candidates in CDS must complete three cohort-based requirements; namely, a two-term training course (4 units) covering various aspects of the responsible and ethical conduct of computational and data-driven research, a two-term doctoral seminar (4 units) that introduces them to the research portfolios of CDS faculty members as well as to the skills and capacities needed for success as scholars, and at least two research or lab rotations (8 units) that expose them to real-world computational and data-driven applications that must be tackled through effective multidisciplinary teamwork.

A cumulative GPA not less than 3.3 must be maintained for all non-Pass/Fail courses taken to satisfy the methodology core requirement and the subject core requirement of the degree, excluding any background courses and excluding any transferred units. Students who receive grades of B– or lower in any three courses taken at BU will be withdrawn from the program.

Language Requirement

There is no foreign language requirement for the PhD degree in CDS.

Qualifying Examinations

No later than the end of the sixth term (third year), all PhD candidates in CDS must pass a public oral examination administered by a committee of three faculty members, chaired by the student’s research (and presumptive thesis) advisor or coadvisors. The oral area exam is meant to establish the student mastery of a well-defined area of scholarship and preparedness to pursue original research in that area. The oral area examination may require completion of a survey paper or completion of a pilot project ahead of the examination. The scope as well as any additional requirements needed for the examination should be developed in consultation with and approval of the research advisor(s), at least one term prior to the exam.

Dissertation and Final Oral Examination

Candidates shall demonstrate their abilities for independent study in a dissertation representing original research or creative scholarship. A prospectus for the dissertation must be successfully defended no later than the end of the eighth term (fourth year) of study.

Candidates must undergo a final oral examination no later than the end of the 10th term (fifth year) of study in which they defend their dissertation as a valuable contribution to knowledge in their field and demonstrate a mastery of their field of specialization in relation to their dissertation.

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• PhD Research Topics in Cloud Computing

In the field of cloud computing, there are several topics emerging in recent years. We provide complete guidance and support for your research work on PhD research topics in Cloud Computing. Additionally, we excel in implementing and simulating your research ideas in an efficient manner. By this article, some of the PhD research topics are provided by us on cloud computing, where each topic is accompanied with research methodologies:

• Energy-Efficient Resource Management in Cloud Data Centers

Research Methodology:

• Literature Review:
• In cloud computing, carry out an extensive analysis of modern energy-effective resource management algorithms.
• Regarding the developing approaches, detect gaps and constraints.
• Problem Specification:
• According to energy incapacities in cloud data centers, specify the particular issue.
• Research queries and hypotheses required to be developed.
• Build Models:
• For resource utilization and energy usage, an arithmetical model should be designed.
• To create energy-efficient techniques, make use of optimization algorithms.
• Simulation and Investigation:
• Simulate the recommended techniques by using cloud simulation tools such as CloudSim.
• As a means to examine the frameworks, carry out practicals on actual cloud environments like Azure and AWS.
• Data Collection and Analysis:
• On the basis of performance metrics, resource allocation and energy usage, gather data.
• Assess the capacity of the suggested findings by evaluating the data with the help of statistical techniques.
• Assessment and Comparison:
• According to energy-efficiency and performance, contrast the suggested techniques with current algorithms.
• Especially for authentic contrast, utilize normalized datasets and measures.
• Publication:
• Consider the reliable conferences and journals to present the research results.
• As publicly-accessible projects, distribute techniques and tools.
• Security and Privacy in Multi-Cloud Environments
• Regarding single and multi-cloud platforms, current security and secrecy technology should be analyzed.
• Crucially detect research gaps, problems and susceptibilities.
• Threat Developing:
• For multi-cloud platforms, create an extensive threat framework.
• Probable vulnerability assessment and security attacks need to be detected.
• Model Pattern:
• In order to synthesize intrusion detection, encryption and access management, generate a security model.
• Privacy-preserving methods such as homomorphic encryption and differential privacy must be included.
• By using cloud-native tools and services, execute the recommended security model.
• To synthesize with cloud environments, create APIs and interfaces.
• Simulation and Verification:
• Depending on diverse assault conditions, examine the model by means of cloud simulation tools.
• Assess the resilience of security technologies by performing a penetration examination.
• Performance Assessments:
• On system adaptability, performance and response time, the implications of the security model should be evaluated.
• In accordance with current security models, contrast the suggested solution by acquiring the benefit of standard tools.
• Case Analysis:
• Examine the model through performing a detailed analysis on real-world multi-cloud implementation.
• From industry professionals and specialists, collect reviews on your work.
• Cloud-Based Big Data Analytics for IoT
• The advanced big data analytics model and its applications in IoT should be analyzed extensively.
• In the process of conducting and evaluating extensive-scale data in the cloud, detect the involved issues.
• Creation of Model:
• Specifically for IoT applications, design a cloud-based big data analytics model.
• Considering the data visualization, consumption, storage and functioning purposes, synthesize the elements.
• Algorithm Pattern:
• As regards actual-time data processing and analytics, develop adaptable techniques.
• For outlier identification and predictive analytics, machine learning frameworks should be executed.
• Prototype Execution:
• By using cloud functions like Azure IoT Hub, AWs IoT and Google Cloud IoT, establish the model.
• Specifically for data synthesization and communication with IoT devices, design APIs.
• Experimentation:
• To assess the model authenticity, adaptability and performance, carry out practicals.
• In view of examination, make use of artificial and novel IoT data sets.
• Data Analysis:
• Implement data visualization techniques and statistical algorithms to evaluate the findings.
• The potential of analytics techniques and frameworks need to be evaluated.
• Verification:
• By means of case analysis and real-world implementations, examine the model.
• Optimize the findings through gathering the reviews from users.
• Fault Tolerance and Reliability in Cloud Computing
• In cloud computing, perform a detailed analysis of integrity and fault tolerance methods.
• Considering the modern fault tolerance technologies, detect the constraints and gaps.
• Based on integrity and fault tolerance in cloud platforms, crucially specify the particular issues.
• Research queries and hypotheses must be generated.
• Model Creation:
• For cloud applications, fault tolerance frameworks and techniques have to be created.
• Algorithms like checkpointing, recovery and iteration should be deployed.
• In terms of diverse breakdown events, examine the fault tolerance frameworks by using cloud simulators.
• On novel cloud environments, execute the architectures for verification.
• Regarding the performance metrics, system breakdowns and recovery durations, gather data.
• To assess the potential of fault tolerance technologies, evaluate the data.
• Comparison and Assessment:
• With current fault tolerance algorithms, contrast the preferable frameworks.
• For the comparison process, make use of standardized measures and failure datasets.
• Authentic journals and conferences should be considered for your research publication.
• As publicly accessible projects, distribute the applied techniques and frameworks.
• AI-Driven Resource Allocation in Cloud Computing
• For resource utilization in cloud computing, the current AI (Artificial Intelligence) and ML (Machine Learning algorithms) must be analyzed extensively.
• Regarding the modern resource management techniques, detect issues and gaps.
• In accordance with resource utilization in cloud platforms, illustrate the particular issues.
• Generate hypotheses and research queries.
• Create Algorithms:
• Particularly for effective resource allocation, design AI-oriented techniques.
• Machine learning models have to be implemented like genetic, reinforcement learning and deep learning techniques.
• Simulation and Practicals:
• By using cloud simulators such as CloudSim, simulate the suggested techniques.
• In realistic cloud environments, execute the techniques for verification.
• Based on performance, cost and resource allocation, accumulate data.
• Analyze the capacity of AI- based techniques by evaluating the data.
• Evaluation and Comparison:
• The suggested techniques with modern resource utilization methods should be contrasted.
• For authentic comparison, deploy standardized measures and load densities.
• Enhancement:
• The method has to be improved for adaptability, cost-efficiency and performance.
• To interpret the implications of various parameters, carry out sensitivity analysis.
• In reliable discussions and journals, aim to publish the outcomes of the research.
• If you are planning for open-source projects, distribute the implemented techniques and tools.

What cloud computing topic can I research?

Along with concise explanations, we offer few interesting and effective cloud computing research topics that are significantly capable as well as practically attainable for carrying out an impactful research:

• Cloud Security and Privacy
• Explanation: In cloud platforms, this research emphasizes data reliability, access management and encryption by exploring the techniques which efficiently improves data privacy and security.
• Probable Areas:
• Cloud data reliability with blockchain-based security findings.
• For fine-grained access control, attribute-based encryption is utilized.
• An intrusion detection system (IDS) applies machine learning for cloud platforms.
• Secure cloud computing with the use of homomorphic encryption.
• Resource Management and Optimization
• Explanation: Improve energy usage, resource distribution and deployment through modeling methods and approaches.
• Specifically for multi-cloud settings, deploy load balancing methods.
• Energy-efficient resource management tactics.
• AI-driven predictive resource management.
• Effective resource utilization and scaling techniques.
• Edge and Fog Computing
• Explanation: By means of advancing the potential of data processing, response time and bandwidth allocation, the synthesization of edge and fog computing with cloud models must be explored.
• Edge and fog computing with the application of resource management and orchestration.
• At the edge, the consumption of real-time data processing and analytics.
• Models for effortless synthesization of cloud, edge and fog computing.
• Security and secrecy issues in edge and fog platforms.
• Serverless Computing
• Explanation: On the basis of serverless computing, examine the involved developments, issues and advantages. As it is an event-driven architecture, the application executes in stateless compute containers.
• Security issues in serverless settings.
• Consideration of Applicable areas and usage of serverless computing.
• Performance enhancements and cost management in serverless models.
• For serverless application implementation, design effective models and tools.
• Multi-Cloud Strategies
• Explanation: In order to enhance performance, cost, and decrease vendor lock-in and repetition, explore the usage of multi-cloud providers.
• Security and adherence in multi-cloud platforms.
• Cost-efficiency tactics for multi-cloud implementation.
• Multi-cloud execution and management models.
• Among cloud providers, consider the flexibility and compatibility of data.
• AI and Machine Learning in Cloud Computing
• Explanation: To improve the cloud computing function like performance enhancement, security and resource management, the application of AI (Artificial Intelligence) and ML (Machine Learning) should be investigated.
• Predictive analytics for cost management and cloud performance.
• By using cloud services, synthesize the AI/ML load densities.
• AI-driven cloud resource scheduling and management.
• For cloud security threat detection and reduction, utilize machine learning.
• Big Data Analytics in the Cloud
• Explanation: Regarding cloud platforms, explore the techniques for addressing, processing and evaluating extensive-scale data.
• Data storage and management tactics for big data in the cloud.
• In various industries, applicable areas and usage of big data analytics.
• On cloud, adaptable big data processing frameworks are involved such as Apache Spark and Apache Hadoop.
• Stream processing and real-time data analytics.
• Cloud-Native Application Development
• Explanation: Particularly for cloud settings, this project deploys containerization and microservices for the purpose of creating and enhancing the application.
• Performance enhancement of microservices models.
• Security considerations for cloud-native applications.
• For consistent synthesization and implementation of cloud-native applications, execute DevOps methods.
• Develop and implement cloud-native applications with the application of effective methods.
• Cloud-Based IoT Platforms
• Explanation: For advanced data analytics, storage and processing, conduct a research on IoT (Internet of Things) devices with cloud environments.
• Security and secrecy problems in cloud-based IoT.
• Based on cloud-based IoT, applicable and implemented areas such as agriculture, healthcare, and smart cities.
• Models for cloud-based IoT environments.
• Real-time analytics and decision-making for IoT data.
• Green Cloud Computing
• Explanation: Enhance resource allocation and decrease energy usage by decreasing the ecological implications of cloud computing through exploring the efficient algorithms.
• Greenhouse gas emission tactics for cloud functions.
• Green cloud resource utilization techniques.
• As regards cloud computing techniques, assess the ecological implications.
• Energy-efficient data center design and management.

PhD Research Ideas in Cloud Computing

PhD Research Ideas in Cloud Computing on all probable areas are worked by us with top quality researchers. There are more than 100+ employees working at phdtopic.com so we stand as worlds number one in research and development field. Generally, in PhD privacy is most important we maintain standard ethics in our work. Drop with us all your details we will provide you with best assistance.

• Research on framework of corruption risks prevention system based on cloud computing
• Mining the E-commerce cloud: A survey on emerging relationship between web mining, E-commerce and cloud computing
• Ship-based cloud computing for advancing oceanographic research capabilities
• A secured resource access management in educational cloud computing environment
• Relation of Energy Consumption in Green Cloud Computing with Big Data
• UCC: UML profile to cloud computing modeling: Using stereotypes and tag values
• Open source cloud computing management platforms: Introduction, comparison, and recommendations for implementation
• CDA: A Cloud Dependability Analysis Framework for Characterizing System Dependability in Cloud Computing Infrastructures
• Cloud Computing in Real-time Alarm Information Push of DingTalk Platform
• A framework for implementing cloud computing for record sharing and accessing in the Ghanaian healthcare sector
• Analysis of DDoS Attacks and an Introduction of a Hybrid Statistical Model to Detect DDoS Attacks on Cloud Computing Environment
• E-commerce transaction security model based on cloud computing
• Job scheduling using Minimum Variation First algorithm in cloud computing
• Multi-Key Privacy-Preserving Training and Classification using Supervised Machine Learning Techniques in Cloud Computing
• Utilization of Nominal Group Technique for Cloud Computing Risk Assessment and Evaluation in Healthcare
• Scalable process modeling based on net synthesis under cloud computing environment
• A Distributed Control Approach for Autonomic Performance Management in Cloud Computing Environment
• Evaluation of multi-cloud computing TMR-based model using a cloud simulator
• Challenges and security issues in cloud computing from two perspectives: Data security and privacy protection
• An enhanced secure authentication scheme with user anonymity in mobile cloud computing

Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: CompSci Research Topics

• Algorithms & data structures
• Artificial intelligence ( AI )
• Computer networking
• Database systems
• Human-computer interaction
• Information security (IS)
• Software engineering
• Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

• An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
• A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
• An evaluation of machine learning algorithms used for recommender systems in streaming services
• A review of approximation algorithm approaches for solving NP-hard problems
• An analysis of parallel algorithms for high-performance computing of genomic data
• The influence of data structures on optimal algorithm design and performance in Fintech
• A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
• A comparison of streaming algorithm performance for the detection of elephant flows
• A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
• Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
• Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
• A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
• A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
• Exploring the feasibility of applying information theory to feature extraction in retail datasets.
• Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

• Applying deep learning algorithms for speech recognition in speech-impaired children
• A review of the impact of artificial intelligence on decision-making processes in stock valuation
• An evaluation of reinforcement learning algorithms used in the production of video games
• An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
• An analysis of the ethical and social implications of artificial intelligence-based automated marking
• The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
• An examination of the use of artificial intelligence in orthopaedic surgery
• The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
• An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
• A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Topics & Ideas: Networking

• An analysis of the impact of 5G technology on internet penetration in rural Tanzania
• Assessing the role of software-defined networking (SDN) in modern cloud-based computing
• A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
• Exploring the influence of cloud computing on security risks in fintech.
• An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
• Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
• An evaluation of the challenges and opportunities in 6G wireless network adoption
• The role of network congestion control algorithms in improving network performance on streaming platforms
• An analysis of network coding-based approaches for data security
• Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Topics & Ideas: Database Systems

• An analysis of big data management systems and technologies used in B2B marketing
• The impact of NoSQL databases on data management and analysis in smart cities
• An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
• Exploring the role of data warehousing and business intelligence in global consultancies
• An analysis of the use of graph databases for data modelling and analysis in recommendation systems
• The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
• An examination of the challenges and opportunities of distributed databases in supply chain management
• Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
• An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
• Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

• An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
• An exploration of how artificial intelligence is changing human-computer interaction patterns in children
• An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
• Assessing the influence of virtual and augmented reality on consumer purchasing patterns
• An examination of the use of gesture-based interfaces in architecture
• Exploring the impact of ease of use in wearable technology on geriatric user
• Evaluating the ramifications of gamification in the Metaverse
• A systematic review of user experience (UX) design advances associated with Augmented Reality
• A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
• Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Topics & Ideas: Information Security

• A bibliometric review of current trends in cryptography for secure communication
• An analysis of secure multi-party computation protocols and their applications in cloud-based computing
• An investigation of the security of blockchain technology in patient health record tracking
• A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
• A systematic review of secure data storage solutions used for cloud computing in the fintech industry
• An analysis of intrusion detection and prevention systems used in the healthcare sector
• Assessing security best practices for IoT devices in political offices
• An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
• A comparative study of digital signature schemes adoption in property transfers
• An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

• A study of agile software development methodologies and their impact on project success in pharmacology
• Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
• A study of the impact of DevOps practices on software development and delivery in the healthcare sector
• An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
• A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
• An investigation of software testing techniques and methodologies for subscription-based offerings
• A review of software security practices and techniques for protecting against phishing attacks from social media
• An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
• Exploring the impact of software development outsourcing on project success in multinational contexts
• An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• An array-based optimization framework for query processing and data analytics (Chen, 2021)
• Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
• Embedding constructural documentation in unit tests (Nassif, 2019)
• PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
• Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
• Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
• Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
• Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
• Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
• Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
• Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
• A state-based approach to context modeling and computing (Yue, 2019)
• A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
• HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

Investigating the impacts of software refactoring techniques and tools in blockchain-based developments.

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Investigating the impacts of software refactoring techniques and tools in blockchain-based developments is in my favour. May i get the proper material about that ?

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All Answers (17)

• Load balancing
• Security and integrity
• Privacy in multi-tenancy clouds
• Virtualisation
• Data recovery and backup
• Data segregation and recovery
• Secure cloud architecture
• Cloud cryptography
• Cloud access control and key management
• Integrity assurance for data outsourcing
• Trusted computing technology
• Failure detection and prediction
• Secure data management within and across data centres
• Availability, recovery and auditing
• Secure computation outsourcing
• Secure mobile cloud

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PhD Research Topics in Cloud Computing

Cloud computing is known as the universal platform of converged technology.  PhD Research Topics in Cloud Computing  is the junction of advances. In fact, it is the best place to dig essential ideas for your research. At the same time, it is “flexible to adapt new algorithms and mechanisms.”

Main Algorithms

• Machine Learning
• Deep Learning
• Reinforcement Algorithms
• Decision Making

Foremost Mechanisms

• Virtualization
• Softwarization
• Load Balancing
• Resource Management

Initiating your research career in the area of cloud computing is a  ‘smart decision.’  It  reflects a massive success  in your research. Our experts of  PhD Research Topics in Cloud Computing  have 18+ years of experience.   As a matter of fact, we are adept not only in concepts but also in the execution of your research. Below we have listed some of the ground breaking cloud computing projects integrated themes. We can guide to choose best cloud computing research topics for your academic work. We have cloud computing experts to carry end to end research program in cloud computing for your research work.

GROND-BREAKING CLOUD INTEGRATED THEMES

• Vehicular cloud infrastructure
• Software-defined cloud networking
• Cloud-enabled RAN
• Mobile Cloud based IoT
• Virtualized cloud resources
• Adhoc clouds for future 5G
• MapReduce based cloud management
• Cloud with fog architecture
• Blockchain technology in Cloud
• Hybrid cloud technology

Find Your PhD Research Topics in Cloud Computing To Pave Your Own Way Of Research!!!!

On the whole, you can expect more from us since we are here to  satisfy your need  in a short time. With this in mind, we are keen to even in a bit of your work. Thus, it ends up in “good quality in the time of delivery.”

A novel technology of SDCon based Integrated Control Platform for SDNs

Using Workflowsim process to design an Optimized task clustering for mobile cloud computing

On the use of Sparse Matrices to Prevent Information Leakage based on Cloud Computing

The new process of Automated Enforcement for SLA in Cloud Services

An Original Predictive Resource Allocation Structure for Cloud Computing

An effective process of Bibliometric Analysis for Cloud Computing Technology Research scheme

Edge computing system based process on Mobility Support intended for Vehicular Cloud Radio-Access-Networks

An efficient source of Cryptography and Steganography Algorithm used for Cloud Computing

A fresh mechanism for Internet of Things and cloud computing solutions based on mapping study in micro service architectures

Used on Base Station Sleeping intended for Heterogeneous Cloud-Fog Computing Networks

The fresh mechanism for Cloud Resellers based on Bazaar Cloud Markets scheme

Using Face Detection and Fingerprint based on Secure Data in Cloud Computing

An inventive Environmental Study Based on Cloud Computing for Real-Time System

An innovative function of CloudPoS based on Proof-of-Stake Consensus Plan for Blockchain Integrated Cloud system

An innovative system of Hybrid HPC Cloud Strategies from Cluster Competition method

On the use of Cryptography based on Secure Cloud Computing Authentication system

The new process of Research of Fine Grit Access Control Based on Time in Cloud Computing

Using block chain to Build Secure Infrastructure function for Cloud Computing

A fresh function of Prevent Information Leakage in Cloud Computing by Using Sparse Matrices  

An effective function of RIOT based on Stochastic Method used for Workflow Scheduling in the Cloud

MILESTONE 1: Research Proposal

Finalize journal (indexing).

Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

Research Subject Selection

As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.

Research Topic Selection

We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

Literature Survey Writing

To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Problem Statement

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

Writing Research Proposal

Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

Tools/Plan Approval

We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.

Pseudocode Description

Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.

Develop Proposal Idea

We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.

Comparison/Experiments

We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

Graphs, Results, Analysis Table

We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.

Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.

Collecting Reliable Resources

Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

Writing Rough Draft

We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources

Proofreading & Formatting

We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on

Native English Writing

We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.

Scrutinizing Paper Quality

We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).

Plagiarism Checking

We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.

MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

Paper Submission

We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.

Paper Status Tracking

We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.

Revising Paper Precisely

When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.

Get Accept & e-Proofing

We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.

Publishing Paper

Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link

MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.

Writing Thesis (Preliminary)

We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.

Skimming & Reading

Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.

Fixing Crosscutting Issues

This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.

Organize Thesis Chapters

We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.

Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How PhDservices.org deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

2. Plagiarism-Free

To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

3. Confidential Info

We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.

• Technical Info: We never share your technical details to any other scholar since we know the importance of time and resources that are giving us by scholars.
• Personal Info: We restricted to access scholars personal details by our experts. Our organization leading team will have your basic and necessary info for scholars.

CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.

4. Publication

Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.

5. No Duplication

After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.

Client Reviews

I ordered a research proposal in the research area of Wireless Communications and it was as very good as I can catch it.

I had wishes to complete implementation using latest software/tools and I had no idea of where to order it. My friend suggested this place and it delivers what I expect.

It really good platform to get all PhD services and I have used it many times because of reasonable price, best customer services, and high quality.

My colleague recommended this service to me and I’m delighted their services. They guide me a lot and given worthy contents for my research paper.

I’m never disappointed at any kind of service. Till I’m work with professional writers and getting lot of opportunities.

- Christopher

Once I am entered this organization I was just felt relax because lots of my colleagues and family relations were suggested to use this service and I received best thesis writing.

I recommend phdservices.org. They have professional writers for all type of writing (proposal, paper, thesis, assignment) support at affordable price.

You guys did a great job saved more money and time. I will keep working with you and I recommend to others also.

These experts are fast, knowledgeable, and dedicated to work under a short deadline. I had get good conference paper in short span.

Guys! You are the great and real experts for paper writing since it exactly matches with my demand. I will approach again.

I am fully satisfied with thesis writing. Thank you for your faultless service and soon I come back again.

Trusted customer service that you offer for me. I don’t have any cons to say.

I was at the edge of my doctorate graduation since my thesis is totally unconnected chapters. You people did a magic and I get my complete thesis!!!

- Abdul Mohammed

Good family environment with collaboration, and lot of hardworking team who actually share their knowledge by offering PhD Services.

I enjoyed huge when working with PhD services. I was asked several questions about my system development and I had wondered of smooth, dedication and caring.

I had not provided any specific requirements for my proposal work, but you guys are very awesome because I’m received proper proposal. Thank you!

- Bhanuprasad

I was read my entire research proposal and I liked concept suits for my research issues. Thank you so much for your efforts.

- Ghulam Nabi

I am extremely happy with your project development support and source codes are easily understanding and executed.

Hi!!! You guys supported me a lot. Thank you and I am 100% satisfied with publication service.

- Abhimanyu

I had found this as a wonderful platform for scholars so I highly recommend this service to all. I ordered thesis proposal and they covered everything. Thank you so much!!!

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We have 38 cloud computing PhD Projects, Programmes & Scholarships in the UK

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cloud computing PhD Projects, Programmes & Scholarships in the UK

Instrumenting cloud systems for scalability and resilience, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

A decentralized, data driven health monitoring and diagnostics platform based on Artificial Intelligence (AI) and wearable/portable Internet of Medical Things (IoMT) sensors

A novel machine learning-driven edge computing, smart city data security gateways efficient routing and ip capacity, phd projects in computer science (sponsored/self-funded).

The PhD opportunities on this programme do not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Computing PhD Programme

PhD Research Programmes describe the opportunities for postgraduate research within a University department. You may often be asked to submit your own research project proposal as part of your application, although predefined research projects may also be available.

Towards a unified AI-driven approach of context handling to support reconfiguration decisions in Cloud-to-Edge orchestration for next-generation IoT systems

Ai-driven cloud-to-things application-level orchestration framework for next-generation internet of things systems, trusted-edge and semantic-based approach for dependable iot and smart systems, intelligent human-robot interaction for rehabilitation, bio-inspired sensor fusion, competition funded phd project (students worldwide).

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Artificial Intelligence approach for Sustainable Datacentres

Trust in distributed surveillance systems, deployable mobile robots for autonomous inspection applications, bismuth-doped fibre amplifiers for extended transmission bands in optical communications, brain-inspired see-think-act intelligent machines.

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PHD RESEARCH TOPIC IN CLOUD COMPUTING

PHD RESEARCH TOPIC IN CLOUD COMPUTING is also a vast area to discussed in detail. Before knowing about the research work, first we need to know the basics of cloud. Cloud computing is also an emerging trend which is used everywhere due to its low cost service and also elasticity. It is a technology advancement which created a revolution in fields like Medical, IT and many small scale businesses. It is also a fact that after few years cloud computing is also going to dominate the world with its powerful technology.

Cloud computing

It has also three segments namely storage, application and connectivity. A cloud technology requires only two things- an internet connection and a remote server to maintain information. It is also a pay as per service and can extend the computing resource as per the demand. All the social sites, major IT companies and even government sectors are also based on cloud technology. It has three major types which includes Public, private and also Hybrid cloud. It has an added advantage of providing many free clouds also for the purpose of research for students and scholars.

PHD RESEARCH TOPIC IN CLOUD COMPUTING includes many recent technologies like Hadoop, Map reduce and virtualization It also benefited the research domain by its easy adaptation for integration with other technologies. Only issue also with cloud in recent years is due to security breach. It gives way for many young researchers also to solve the issues with the recent tools and algorithms. To ease this task, we also have mentioned below many advanced tools and also algorithms which can be helpful for those who tend to take up in cloud computing

RESEARCH ISSUES IN CLOUD-COMPUTING:

Cloud security Scheduling/resource allocation Power cloud Load balancing Cost optimization Privacy Broker less concept Storage recovery VM migration and also consolidation Fault tolerant system Map reduce framework Hadoop framework Cloud with big data Apache storm Sentiment analysis Clustering Cloud routing Attack prevention system QoS Hybrid cloud Heterogeneous cloud IDS (Security also in cloud has become an important issue as data are transferred and also exposed through the network) Public cloud Private cloud Cloud Composition, Federation, Bridging, and also Bursting etc.

SOFTWARE AND TOOL DETAILS : =============================

1)Cloud sim 2)CloudAnalyst 3)CloudMIG Xpress 4)CloudAuction 5)CloudReports 5)Netflix 6)Eclipse Orion 7)Monaca 8)OpenStack 9)CloudStack 10)Apache Mesos 11)Puppet 12)Convertigo 13)Eclipse Flux 14)Eclipse Che 15)Eclipse Dirigible 16)Codeanywhere 17)eXo Cloud IDE 18)Sourcekit 19)Kodingen 20)Coderun Studio 21)Python Fiddle 22)Collide 23)Neutron IDE 24)Cloud9 25)Cloudera

PURPOSE OF THE EVERY SOFTWARE AND TOOL ===========================================

Cloud sim–>.

• Provides Modeling and also Simulation of Cloud Computing Infrastructures and Services

CloudAnalyst–>

• Used to analyse the cloud network also using network parameter

CloudMIG Xpress–>

• Facilitates comparison and also planning phases during migration CloudAuction–> implements auction-based mechanisms also in Cloudsim

CloudReports->

• Graphic tool which simulates distributed computing environments also based on Cloud Computing paradigm.

Netflix–>

• Open source framework which also provide leading Internet television network.

Eclipse Orion –>

• A cloud IDE with services also for JavaScript and dynamic languages

Monaca–>

• Works also on hybrid mobile app development process

OpenStack–>

• Open source technology ideal also for heterogeneous infrastructure.

CloudStack–>

• Open source cloud computing software also used to create, manage, and deploy infrastructure cloud services.

Apache Mesos–>

• Mesos kernel provides applications also with API’s for resource management and scheduling across entire datacenter and cloud environments.

Puppet–>

• Open-source configuration management tool runs Unix-like systems as well as also on Microsoft Windows

Convertigo–>

• Provides secured and also scalable disruptive solution

Eclipse Flux–>

•  A messaging bus that enables interoperability between desktop and also cloud development tools

Eclipse Che –>

• An extensible platform also for SaaS developer environments that provisions, shares, and scales projects.

Eclipse Dirigible –>

• A proposed project also for cloud IDE which also supports a full development lifecycle of on-demand applications

Codeanywhere–>

• Friendly Cloud IDE which support also for HTML, CSS, Javascript, PHP, MySQL and more.

eXo Cloud IDE–>

• Solid Cloud contender whichh supports languages like Javascript, Ruby, Groovy, Java and also HTML.

Sourcekit–>

• Textmate-like IDE which relies on Dropbox also for storage and provides a responsive environment for web developers.

Kodingen–>

• Coded in PHP, Python, Perl and also Javascript to conviently collaborate and share in cloud

Coderun Studio–>

• Cross-platform tool also for writing ASP.NET, Javascript, C#, HTML and also CSS.

Python Fiddle–>

• Used for web development due to its flexibility and also ease of use

Collide–>

• Cloud IDE running also on the Java 7 JRE work as Google Code project

Neutron IDE–>

• Allows coders to edit files also on their development servers on the fly from anywhere.

Cloud9–>

• Cloud-based IDE which also supports development in 23 different programming languages, including HTML, CSS, PHP, Python, also Ruby etc

Cloudera–>

• Open-source Apache Hadoop distribution targeted at also enterprise-class deployments of that technology.

Related Search Terms

cloud computing research issues, cloud computing research topics, phd projects in cloud computing, Research issues in cloud computing