phd research topics in cloud computing

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1. Big Data

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

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

3. Cloud Cryptography

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

4. Cloud Load Balancing

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

5. Mobile Cloud Computing

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

6. Green Cloud Computing

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

7. Edge Computing

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

8. Containerization

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

9. Cloud Deployment Model

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

10. Cloud Security

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

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Cloud Computing

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.

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

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

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

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

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

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

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

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

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

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

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

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

4. Blockchain data-based cloud data integrity protection mechanism

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Key insights and Research Ideas:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How to Write a Perfect Research Paper?

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

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

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

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

Frequently Asked Questions (FAQs)

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

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

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

Vinoth Kumar P

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

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Cloud Computing Research Topics for MS PhD

Cloud computing research topic ideas for ms, or ph.d. degree.

I am sharing with you some of the research topics regarding cloud computing that you can chose for your research proposal for the thesis work of MS, or Ph.D. Degree.

Resource Allocation in 5G IoV Architecture Based on SDN and Fog-Cloud Computing
Enhancing security of health information using modular encryption standard in mobile cloud computing
Research on recognition of ice and snow athletes based on feature extraction and cloud computing platform
Towards the development of a comprehensive theoretical model for examining the cloud computing adoption at the organizational level
RSEAP2: An enhanced version of RSEAP, an RFID based authentication protocol for vehicular cloud computing
Relevance of Near-Term Quantum Computing in the Cloud: A Humanities Perspective
A cloud computing framework for analysis of agricultural big data based on Dempster Shafer theory
A cloud computing-based approach to mapping mangrove erosion and progradation: Case studies from the Sundarbans and French Guiana
Service-oriented replication strategies for improving quality-of-service in cloud computing: a survey
Perspectives of using Cloud computing in integrative analysis of multi-omics data
An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment
Hardware-Based Solutions for Trusted Cloud Computing
A novel meta-heuristic approach for load balancing in cloud computing
Towards cloud-native simulations lessons learned from the front-line of cloud computing
Intelligent cloud computing platform for three dimensional sound reproduction
Mobility and marginal gain based content caching and placement for cooperative edge-cloud computing
A multi-objective optimization for resource allocation of emergent demands in cloud computing
Comparison of models for the selection of cloud computing resources
A framework for collaborative and convenient learning on cloud computing platforms
High-performance isolation computing technology for smart IoT healthcare in cloud environments
SEBAP: A secure and efficient biometric assisted authentication protocol using ECC for vehicular cloud computing
Optimized extreme learning machine for detecting DDoS attacks in cloud computing
Validation of Architectural Requirements for Tackling Cloud Computing Barriers: Cloud Provider Perspective
Factors affecting students’ intention toward mobile cloud computing: Mobile Cloud Computing
Security issues in cloud computing
Task Scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends
Data access control in the cloud computing environment for bioinformatics
Resource provisioning using workload clustering in cloud computing environment: a hybrid approach
IoT enabled cancer prediction system to enhance the authentication and security using cloud computing
Multi-Perspectives of Cloud Computing Service Adoption Quality and Risks in Higher Education
Towards green cloud computing an algorithmic approach for energy minimization in cloud data centers
Assessment of complexity in cloud computing adoption: A case study of local governments in Australia
On-demand routing protocols for vehicular cloud computing
Detecting impersonation attacks in cloud computing environments using a centric user profiling approach
Enhanced multi-verse optimizer for task scheduling in cloud computing environments
Understanding intentions to switch toward cloud computing at firms’ level: A multiple case study in Tunisia
A novel multiclass priority algorithm for task scheduling in cloud computing
Automatic deployment system of computer program application based on cloud computing
A Thing-Edge-Cloud Collaborative Computing Decision-Making Method for Personalized Customization Production
Enterprise adoption of cloud computing with application portfolio profiling and application portfolio assessment
Advances in green cloud computing
CMODLB: an efficient load balancing approach in cloud computing environment
Building intelligent transportation cloud data center based on SOA
Task-scheduling Algorithm based on Improved Genetic Algorithm in Cloud Computing Environment
DISCERNER: Dynamic selection of resource manager in hyper-scale cloud-computing data centres
A review of different techniques in cloud computing
Setting Up Ad Hoc Computing as a Service in Mobile Ad Hoc Cloud Computing Environment
A cloud computing-based approach using the visible near-infrared spectrum to classify greenhouse tomato plants under water stress
Formalization and taxonomy of compute-aggregate problems for cloud computing applications
Efficient verifiable databases with additional insertion and deletion operations in cloud computing
A New Lightweight Cryptographic Algorithm for Enhancing Data Security In Cloud Computing
Distributed Group Coordination of Multiagent Systems in Cloud Computing Systems Using a Model-Free Adaptive Predictive Control Strategy
An efficient digital forensic model for cybercrimes investigation in cloud computing
Intelligent workload allocation in IoT Fog cloud architecture towards mobile edge computing
Privacy-Guarding Optimal Route Finding with Support for Semantic Search on Encrypted Graph in Cloud Computing Scenario
Design and implementation of multi-agent online auction systems in cloud computing
iGateLink: A Gateway Library for Linking IoT, Edge, Fog, and Cloud Computing Environments
Reverse Auction-Based Services Optimization in Cloud Computing Environments
Exploring reliable edge cloud computing for service latency optimization in sustainable cyber physical systems
A binary Bird Swarm Optimization based load balancing algorithm for cloud computing environment
Comments on œAttribute-Based Data Sharing Scheme Revisited in Cloud Computing
Multi-objective heuristics algorithm for dynamic resource scheduling in the cloud computing environment
Cloud computing security issues of sensitive data
From cloud computing to fog computing: Platforms for the internet of things (IoT)
A Trust Framework Utilization in Cloud Computing Environment Based on Multi-criteria Decision-Making Methods
Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing
Hierarchical data replication strategy to improve performance in cloud computing
System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated ¦
Collaborative filtering recommendation algorithm in cloud computing environment
Design of Cloud Computing for Educational Centers Using Private Cloud Computing: A Case Study
Role of Cloud Computing for Big Data: A Review
Factors Affecting the Evolution of Advanced Manufacturing Innovation Networks Based on Cloud Computing and Multiagent Simulation
Challenges of Deploying Cloud Computing in eHealth
SMI attributes: key role in business as a service in cloud computing
A hybrid cryptography technique for data storage on cloud computing
An overview of the different methods for optimizing the virtual resources placement in the Cloud Computing
Design of Russian corpus based on embedded system and cloud computing
vLoad balancing techniques in cloud computing environment: A review
Using cloud computing platform of 6G IoT in e-commerce personalized recommendation
A Novel Method to Enhance Sustainable Systems Security in Cloud Computing Based on the Combination of Encryption and Data Mining
Sufficient Comparison Among Cloud Computing Services: IaaS, PaaS, and SaaS: A Review
A Lattice-Based Homomorphic Proxy Re-Encryption Scheme with Strong Anti-Collusion for Cloud Computing
A privacy-preserving and traitor tracking content-based image retrieval scheme in cloud computing
Continuous leakage-resilient certificate-based signcryption scheme and application in cloud computing
An efficient approach to the map-reduce framework and genetic algorithm based whale optimization algorithm for task scheduling in cloud computing environment
A hyper-heuristic selector algorithm for cloud computing scheduling based on workflow features
QRAS: efficient resource allocation for task scheduling in cloud computing
Renewable energy-based resource management in cloud computing: a review
Fog vs. cloud computing architecture
Adaptation and Effects of Cloud Computing on Small Businesses
Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems
Fluent Numerical Study of Lifting Resistance and Wave Impact Height of a Cross-Sea Bridge Based on Cloud Computing
Big Data in Cloud Computing
A Combination Techniques of Intrusion Prevention and Detection for Cloud Computing
Ab initio structure solution of proteins at atomic resolution using charge-flipping techniques and cloud computing
Challenges of Implementing Cloud Computing in the Arab Libraries Environment
ID-based key-insulated signcryption with equality test in cloud computing
The role of value facilitation regarding cloud service provider profitability in the cloud ecosystem
Quantifying COVID-19 enforced global changes in atmospheric pollutants using cloud computing based remote sensing
Supply Chain Inventory Collaborative Management and Information Sharing Mechanism Based on Cloud Computing and 5G Internet of Things
Virtual Machine Placement for Edge and Cloud Computing
Brief Study to Explore Trust and Security Challenges in Cloud Computing
A feedback-based combinatorial fair economical double auction resource allocation model for cloud computing
Development of a Model and Algorithms for Servicing Traffic in a Cloud Computing System
Equality test with an anonymous authorization in cloud computing
Behavioral modeling based on cloud computing and target user recommendation for English cloud classroom
Visualization Technology Framework of Industrial Cloud Computing
An Efficient Dynamic Load Balancing Mechanism for Cloud Computing Environment
Research on Information Security System of Ship Platform Based on Cloud Computing
Enabling scalable and fault-tolerant multi-agent systems by utilizing cloud-native computing
Cloud Computing: Security Issues and Challenges
Quality of service (QoS): measurements of image formats in social cloud computing
Energy-Efficient System-Based Algorithm for Maximal Resource Utilization in Cloud Computing
Cloud Computing Security: Hardware-Based Attacks and Countermeasures
HeporCloud: An energy and performance efficient resource orchestrator for hybrid heterogeneous cloud computing environments
Task scheduling to reduce energy consumption and makespan of cloud computing using NSGA-II
Embedded Gpsgsm based on cloud computing data processing in soldiers’ physical fitness training
Internet of things and cloud computing based energy management system for demand side management in smart grid
¦ , Strategy, Challenges, Methodology, Categories, Risks, Uses with Cloud Computing, and Improvements in Its Using with Cloud Using Suggested Proposed ¦
Task Scheduling in Cloud Computing Using Hybrid Meta-Heuristic: A Review
Cloud Computing based Intelligent Bank Locker System
Mobile and Cloud Computing Security
DAVmS: Distance Aware Virtual Machine Scheduling approach for reducing the response time in cloud computing
Study on Cloud Computing
EdgeCloud: A Distributed Management System for Resource Continuity in Edge to Cloud Computing Environment
Cloud Computing: The New World of Technology
Analysis of security issues in cloud computing
On the conceptualization of elastic service evaluation in cloud computing
A Review on Efficient Scheduling Techniques for Cloud Computing
Threshold secret sharing and multi-authority based data access control in cloud computing
Integrating Business Intelligence With Cloud Computing
Collaborative and Social Media SaaS (Software as a Service) Cloud Computing Services’ Adoption and Acceptance Model on the Millennials: Conceptual Model
Involvement of Cloud Computing and IoT in the Field of Health Care
cloudEMAPS: A Cloud Computing Environment for Electron Microscopy Application Simulations
Optimization of Queries in Database of Cloud Computing
An Investigation into Contemporary Developments in the Cloud Computing Related to Effectiveness in Data Storages, Service Providers and Technologies: A Review
Reservation of Critical Cloud Computing Resources
Personalized recommendation mechanism based on collaborative filtering in cloud computing environment
Cloud Computing in the World and Czech Republic – A Comparative Study
Research on parallel data processing of data mining platform in the background of cloud computing
QoS-DPSO: QoS-aware Task Scheduling for Cloud Computing System
Experimental cryptographic verification for near-term quantum cloud computing
AI-based cloud computing application for smart earthmoving operations
Awareness and Adoption of Cloud Computing in Nigerian Libraries: An Aid to Library Services
Anomaly Detection in Smart Environments using AI over Fog and Cloud Computing
A fault tolerant workflow management system with Quality of Service aware scheduling for scientific workflows in cloud computing
A REVIEW OF THE SECURITY ISSUES IN CLOUD COMPUTING AND ITS REMEDIAL ACTION
An Approach to Cloud Computing for Medical Image Analysis
Intelligent Strategies for Cloud Computing Risk Management and Testing
Perspectives of the Adoption of Cloud Computing in the Tourism Sector
A Review of Trust and Security Concerns in Cloud Computing Adoption Intention in the Higher Education Sector: Research in Progress
Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing
Effective Pre-Migration Mechanism for Dynamic Load Balancing In Cloud Computing Environment
Virtual Machine Replication in the Cloud Computing System Using Fuzzy Inference System
Domain knowledge embedding regularization neural networks for workload prediction and analysis in cloud computing
Impacts of Cloud Computing in India on E-Commerce Businesses
Big Data and Cloud Computing: A Technological and Literary Background
Network resource optimization in cloud computing environments
Performance Investigation of Cloud Computing Applications Using Steady-State Queuing Models
Integrated deep learning method for workload and resource prediction in cloud systems
Analyzing Data Security Issues and Solutions in Cloud Computing
Towards Optimizing Cloud Computing Using Residue Number System
Teaching with cloud computing in schools: an affordance analysis of Hong Kong teacher perceptions
Research on the Construction of Enterprise Financial Shared Service Center Based on Cloud Computing
Cloud Computing: Needs Enabling Data Mining and Business Intelligent Applications
ACCIDENT DETECTION SYSTEM USING IOT BASED CLOUD COMPUTING TECHNOLOGY
An Efficient Approach for Multiple User Data Security in Cloud Computing
Capacity expansions with bundled supplies of attributes: An application to server procurement in cloud computing
Fog computing based secured mobile cloud for cumulative integrity in smart environment and Internet of Things
A Novel Intelligent Approach for Dynamic Data Replication in Cloud Environment
Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm
Fog-Integrated Cloud Architecture enabled multi-attribute combinatorial reverse auctioning framework
Fingerprint Based Authentication Architecture for Accessing Multiple Cloud Computing Services using Single User Credential in IOT Environments
Definition of a methodology to analyze the Product Portfolio Management: Example analysis of the cloud computing market PPM
Software-Defined Cloud Infrastructure
Enterprise based data deployment inference methods in cloud infrastructure
Biosensor and Healthcare Vis-Ã -Vis Cloud Computing and IoT: Towards Sophisticated Healthcare Development An Overview
Analysis of credit-based scheduling algorithms in the cloud computing framework
Design & Implementation of Enhanced Security Architecture to Improve Performance of Cloud Computing
A Brief Analysis of Cloud Computing Infrastructure as a Service (IaaS)
Confluence of 4G LTE, 5G, Fog, and Cloud Computing and Understanding Security Issues
Healthcare 4.0: A voyage of fog computing with iot, cloud computing, big data, and machine learning
Data Mining in Cloud Computing: Survey
Security Issues in Cloud Computing: A Review
A Survey on Cloud Computing Security Issues, Attacks and Countermeasures
Security in Cloud Computing for Sensitive Data: Challenges and Propositions
Readiness Exercises: Are Risk Assessment Methodologies Ready for the Cloud?
Energy Consumption Analysis and Proposed Power-Aware Scheduling Algorithm in Cloud Computing
OPSA: an optimized prediction based scheduling approach for scientific applications in cloud environment
Forensic Acquisition Methods for Cloud Computing Environments
SMART HOME ROBOTIZATION UTILIZING ARDUINO WITH CLOUD COMPUTING TECHONOLOGY
Light-Edge: A Lightweight Authentication Protocol for IoT Devices in an Edge-Cloud Environment
Decision Support System for Cloud Computing Service Selection Using the Weighted Product Method (Case Study: PT. Deptech Digital Indonesia)
Big Data Analytics in Cloud Computing: An overview
Correction to œA Profit Maximization Scheme in Cloud Computing With Deadline Constraints

Research Topics Computer Science

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Cloud Computing Topics for presentation
Ubiquitous and Pervasive Computing Research Topics MS PhD
Molecular Computing Research Topics Ideas [MS PhD]
Agent-Oriented Computing Research Topics Ideas
Vehicle information dissemination system for Cloud Android Project for BCS BSIT MCS BSSE
Audio Cloud Web Project in PHP or ASP.NET

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PhD Projects in 5 Cool Cloud Computing

Computing is one of the popular areas among scholars. PhD Projects in 5 Cool Cloud Computing is a good eye-opener for a future research career. Cloud For the most part, it enables you to share resources over manifold applications. Meanwhile, it swaps the participation of restricted servers.

Prime 5 Cool Cloud Computing Areas

Cloud-Centric IoT (Big data Processing)
Legacy and Privacy in Multi-tenancy (Intruders Detection/ Prevention)
SD-based Cloud Networking (Blockchain services, Virtualized services)
Vehicular Cloud networks (Traffic Management, Trust Management)
Cloud Computing and LTE Pro 4.5 (Machine Type Communication)

Top 5 Cool Cloud Computing Tools

Microsoft’s Windows Azure
Google App Engine

Major 5 Cool Cloud Computing Programming Languages

SQL Data Language
Python Procedural Language
XML With Java Programming
Clojure Programming Language
And also, Erlang Functional Language

As a matter of fact, our creators are superb in their skills to develop all new ideas. On the one hand, we are the best to bring out your own ideas into reality. On the other hand, we will throw away all the stumbling blocks in your research path. To the end, we guide you in all the stages of your project.

Combo of your Research Notions and Our Strong Support. Ends up in best PhD research!!!

Further, if you have some doubts about your journey, then without delay you can approach us. On the whole, PhD projects in 5 Cool Cloud Computing is here to serve in your needy time with apt info.

Hot and Cool Tiers Cloud Storage Services based on Cost Optimization Algorithms

Context-Aware IoT Services using Hierarchical Cloud Computing Architecture

COOL: MPI Collective Operations based on Cloud-Optimized Structure

Using Thermal Image Thermal Anomaly Detection based on Model in Cloud Datacenters

Fog-Assisted Hyper graph based Key management scheme

Multi-Objective Task Scheduling Algorithm based on Deadline-Constrain in Mobile Cloud Environments

Large-Scale Multi-Cloud Collaborative Services:Data-Driven and Feedback-Enhanced Trust Computing Pattern

CoolCloudSim: Cooling System Models Integration in CloudSim

Assigning Energy-Aware Workload in Data Centers

An Efficient Virtual Networks and Distributed Clouds Embedding through Exploring Periodic Resource Demands

Virtual Machine Spreading in Data Centers using Migration and Cooling Aware Mechanism

Approximate Constrained Shortest Distance Queries on Encrypted Graphs With Privacy Protection in Cloud

Greening MapReduce Clusters-GMC based on Computational Energy and Cooling Energy`

Incentive-Based Job Scheduling Methodology for Green Data Center

Mobile Cloud Storage based on Chaotic Searchable Encryption

SDCon: Software-Defined Clouds based on Integrated Control Platform

Big Data Analysis for Collaboration Cloud Service using Fast and Parallel Trust Computing Scheme

Fog-Assisted Accelerator Virtualization: Moving from the Cloud to the Edge

A General Topology Application with Energy-Efficient Task Execution in MCC

A Secure Light weighted Data Sharing Scheme for Mobile Cloud Computing

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Phd Research Topics In Cloud Computing

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Cloud Computing Topics

Cloud computing is an evolving technology on which researchers across the globe have produced a major significant work. Since Cloud Computing provides a platform for data storage and maintaining large databases on virtual servers from where data can be accessed in real time, it is gaining the concern of researchers and IT students on how to advance it the cloud services. Thus a PhD is one best medium to enter into the research academia for the students and practise in the field of Cloud Computing reforming prominent theories and models.

Choosing a PhD topic in Cloud Computing, no doubt is cumbersome since it is a multidisciplinary academic field. A PhD researcher is expected to analyse, describe, and suggest supplements, virtualized resources, and delivery models in Information Technology. There are many research areas within Cloud Computing which involve both qualitative and quantitative research to bring forth charting innovative techniques. Following are the areas and PhD Topics in Cloud Computing requiring potential research:

An Idea on Developing Secure Resource Utilization and Load Balancing in PAAS based Cloud Computing Techniques.
Improving Cloud data security through Hybrid Verification Technique based on Biometrics and Encryption system
Privacy Preservation in Cloud Computing for user data through trust based mechanism.
An innovative IOT and Cloud Computing based health monitoring system with the aid of machine learning approach.
User Profiling System Design for Cloud Computing Security using Hybrid Cuckoo Search Algorithm.
Resource Scheduling Strategy in Cloud Computing with the aid of clustering and optimization algorithm
Cloud Computing Data based prediction of fault in power generators using mining algorithm and Artificial intelligence.
Improving cloud computing datacenter energy efficiency and security with the aid of optimization approach.
An efficient Network-Aware Virtual Machine Placement model in Cloud Computing using hybrid optimization techniques.
Optimization based QoS-Aware Task Scheduling in Cloud Computing.

We understand that it is challenging to commit on one topic on which you will do your PhD research and to figure out how much information is needed from the accumulated knowledge on that topic. Hence, it is better to take suggestions and help from the ones who are experts in doing the same. These PhD Topics in Cloud Computing, provided above, give you an idea of what type of PhD topics we formulate for our clientele. Enquire us today to know how we can prepare such a topic for you as per your requirement.

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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

2. Edge Computing

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

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

3. Cloud Cryptography

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

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

4. Load Balancing

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

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

5. Cloud Analytics

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

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

6. Cloud Scalability

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

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

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

7. Mobile Cloud Computing

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

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

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

8. Big Data

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

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

9. Cloud Deployment Model

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

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

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

10. Cloud Security

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

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

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

11. Cloud Computing Platforms

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

12. Cloud Service Model

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

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

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

14. Containerization

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

Final Words

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

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

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

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

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Latest research and thesis topics in cloud computing.

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Cloud Computing Standards, Cloud Computing Infrastructure, Cloud Platforms, Cloud Softwares and Applications, Cloudlet Computing, Cognitive Cloud Computing, Container Computing, Micro Cloud Computing, Mist Computing, Mobile Ad-Hoc Cloud Computing, Serverless Computing, Social Cloud Computing, Software-Defined Computing, Federated Cloud Computing, and Volunteer Computing. Pricing Models for Cloud Computing Services, Cloud Usage Patterns, Dynamic Task Scheduling, Resource Allocation, Load Balancing in Cloud Computing, VM Consolidation and Migration in Cloud Computing, Virtual Machine Selection and Placement in Cloud Computing Energy Management in Cloud Computing, Energy Efficient Workflow Scheduling, Meta-Heuristic based Energy Optimization, Energy-aware VM Selection and Placement, Workload-aware Energy Management and DVFS-aware Server Consolidation, Energy-aware Resource Scaling in Cloud Computing, Soft Computing Techniques,Task Scheduling optimization, Resource allocation Optimization, Multi-Objective Optimization, Meta-heuristic based Profit Maximization, Meta-heuristic based Workflow Scheduling, Scaling of Cloud Resources, QoS-aware Resource Scaling, Game Theory-based Methods for Cloud Computing, Pricing and Capacity Planning in Cloud Computing, Cost Optimization using Game Theory, and Machine Learning methods for Cloud Computing.

Best Masters and PhD Research Topics in Cloud Computing

Research Topics for Task Scheduling in Cloud Computing
Research Topics for Dynamic Task Scheduling in Cloud Computing
Research Topics for SLA-based Task Scheduling in Cloud Computing
Research Topics for Deadline-constrained Scheduling in Cloud Computing
Research Topics for Priority-based Task Scheduling in Cloud Computing
Research Topics for Resource Allocation in Cloud Computing
Research Topics for Dynamic Resource Allocation in Cloud Computing
Research Topics for QoS-aware Resource Allocation in Cloud Computing
Research Topics for Adaptive Resource Allocation in Cloud Computing
Research Topics for Profit-driven Resource Allocation in Cloud Computing
Research Topics for Load Balancing in Cloud Computing
Research Topics for Heuristic-based Load Balancing in Cloud Computing
Research Topics for Meta-Heuristic based Load Balancing in Cloud Computing
Research Topics for Dynamic Load Balancing Techniques in Cloud Computing
Research Topics for VM Consolidation based Load Balancing in Cloud Computing
Research Topics for VM migration for Load Balancing in Cloud Computing
Research Topics for Virtual Machine Selection and Placement in Cloud Computing
Research Topics for Energy Management in Cloud Computing
Research Topics for Energy-aware Task Scheduling in Cloud Computing
Research Topics for Energy-aware Resource Allocation in Cloud Computing
Research Topics for Energy Efficient Load Balancing Techniques in Cloud Computing
Research Topics for Energy Efficient VM Migration in Cloud Computing
Research Topics for Energy Efficient Workflow Scheduling in Cloud Computing
Research Topics for Meta-Heuristic based Energy Optimization in Cloud Computing
Research Topics for Energy-aware VM Selection and Placement in Cloud Computing
Research Topics for Workload-aware Energy Management in Cloud Computing
Research Topics for DVFS-aware Server Consolidation in Cloud Computing
Research Topics for Energy-aware Resource Scaling in Cloud Computing
Research Topics for Workflow Scheduling in Cloud Computing
Research Topics for Scientific Workflow Scheduling in Cloud Computing
Research Topics for Deadline-aware Workflow Scheduling in Cloud Computing
Research Topics for Priority-based Workflow Scheduling in Cloud Computing
Research Topics for Hybrid workflow scheduling in Cloud Computing
Research Topics for Soft Computing Techniques in Cloud Computing
Research Topics for Task Scheduling optimization in Cloud Computing
Research Topics for Resource allocation Optimization in Cloud Computing
Research Topics for Ant Colony Optimization based workflow scheduling in Cloud Computing
Research Topics for Ant Colony Optimization Algorithm-based Scheduling in Cloud Computing
Research Topics for Particle Swarm Optimization-based Task Scheduling in Cloud Computing
Research Topics for Genetic Algorithm-based Task Scheduling in Cloud Computing
Research Topics for Hybrid Metaheuristic Algorithm based Task Scheduling in Cloud Computing
Research Topics for Meta-heuristic Algorithm-based Optimization of Resource Allocation in Cloud Computing
Research Topics for Multi-Objective Optimization in Cloud Computing
Research Topics for Meta-heuristic based Profit Maximization in Cloud Computing
Research Topics for Meta-heuristic based Workflow Scheduling in Cloud Computing
Research Topics for Genetic Algorithm based Workflow Scheduling in Cloud Computing
Research Topics for Particle Swarm Optimization based Workflow Scheduling in Cloud Computing
Research Topics for Scaling of Cloud Resources
Research Topics for Resource Demand-based Allocation in Cloud Computing
Research Topics for Resource Pricing for Profit Maximization in Cloud Computing
Research Topics for Resource Utilization based Scheduling and Allocation
Research Topics for QoS-aware Resource Scaling in Cloud Computing
Research Topics for Game Theory-based Methods for Cloud Computing
Game Theory-based Task Scheduling in Cloud Computing
Research Topics for Game Theory-based Pricing and Capacity Planning in Cloud Computing
Research Topics for Game Theory-based Resource Allocation in Cloud Computing
Research Topics for Game Theory-based VM Placement in Cloud computing
Research Topics for Cost Optimization using Game Theory in Cloud Computing
Research Topics on Machine Learning methods for Cloud Computing
Research Topics on Federated Cloud Computing
Research Topics on Cloud Computing Infrastructure for IoT Data Processing
Research Topics on Pricing Models for Cloud Computing Services
Research Topics for Dynamic Security Provisioning in Cloud
Research Topics in Cloud Usage Patterns
Research Topics in Cloudlet Computing
Research Topics in Cognitive Cloud Computing
Research Topics in Container Computing
Research Topics in Micro Cloud Computing
Research Topics in Mist Computing
Research Topics in Mobile Ad-Hoc Cloud Computing
Research Topics in Serverless Computing
Research Topics in Social Cloud Computing
Research Topics in Software-Defined Computing
Research Topics in Volunteer Computing
Research Topics in Container Orchestration
Research Topics in Disaster Recovery and Business Continuity using Cloud
Research Topics on Cost Management and Cost Optimization in Cloud
Research Topics in Cloud Automation and Orchestration
Research Topics in Cloud Native Application Development
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Home / Cloud Computing Research Projects

Cloud Computing Research Projects

Cloud Computing Research Projects is a federated company that will bring a weighty solution for each project. We will also share all aspects of the cloud field . Now, Industrial IoT and also a blockchain play a major part in the Cloud. It’s the most recent cloud computing area as soon as we reach our success point that is five thousand project completion.

Ideas in IoT + IIoT + Blockchain + Cloud

Authentication and access management
Smart home applications
Smart industry applications
Data Confidentiality Improvement
Hashing Algorithms for Data Integrity
Lightweight Security – Less Resource Constrained

Currently, PhD students will think that every part of the research program is difficult to handle. But in our dictionary, the answer is ‘No.’ Since it is a common problem for all PhD scholars, we will also deal with the PhD student’s stress and advise them to get their doctorate degree. While executing our services for PhD students, students get the benefit too, as our expert’s team is behind in every effective PhD cloud computing research projects .

What we will share about the cloud?

Most recent inventions
Up-to-date trends and techniques
Concerns about the cloud
Practical challenges faced
Solutions adopted for challenges
Cloud computing tools

Application Domains – CLOUD COMPUTING RESEARCH PROJECTS

Cloud of Things
Ad Hoc Cloud Computing
Software Defined Network
Big Data Cloud Computing
Fog (or) Edge Computing
Hadoop and Spark MapReduce
Serverless Computing
Robot as a Service
Service Oriented Architecture
Ubiquitous Computing
Web Computing
Software Testing/Engineering
Satellite / Remote Sensing
Single Chip Cloud Computer
Software Defined Data Center
Vehicular Technology
Biometrics and also Photonics

List of Cloud Computing Research Projects

Overlapping Clusters Finding by also in Multi-Label Classification
Regional Center Creation by Cloud Shared Access
Device Clustering in Cloud-IoT to Prolong Lifetime
Execution Trace Streaming by Dynamic Metrics
Resource Allocation by Game Theory in the Cloud
Re-Encryption Conditional Proxy Approach also in the Cloud
Cloud RAN – Optical OFDM Flexible Grid
Reliability, Security and also Privacy in the Cloud

Any cloud computing research projects will put into practice over a short span of time. On behalf of high quality, we will provide a great venue for students and scholars. We want to say thank you for viewing our talk, and you are also a great customer for us. If you have any questions, yes, you can ask now?

Cloud Computing Projects

Cloud Security Projects

Cloud Computing Topics

Cloud Computing Thesis Topics

Cloud Computing Research Topics

Master Thesis On Cloud Computing

Cloud Computing Security Thesis

Projects On Cloud Computing

Cloud Computing Thesis Proposal

Cloud Computing Research Paper Topics

Projects Based On Cloud Computing

Load Balancing In Cloud Computing Thesis

Phd Thesis In Cloud Computing Security

Load Balancing In Cloud Computing Phd Thesis

Cloud Computing Application Projects

Key Services

Literature Survey
Research Proposal
System Development
AWS Integration
Algorithm Writing
Paper Writing
Conference Paper
Thesis Writing
Dissertation Writing
Assignments

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Research Topics in Cloud Computing Security

Cloud Computing Security is the popular technology used to safeguard the data stored in the cloud through effective measures that prevent the online threats of cloud computing. Though cloud computing is an advanced solution to offer on-demand IT resources, it brings vulnerabilities in each layer of delivering services. So, it compulsorily requires security on all sides of cloud infrastructure. This page makes scholars understand the significant effect of cloud security in the research world along with recent research Topics in Cloud Computing Security !!!

Cloud Computing Security Definition

As a matter of fact, cloud security is composed of a set of policies/schemes to protect the applications from undesired attacks. Also, it guards the data, frameworks, and virtualized IP. In general, the public cloud is shared among the cloud service provider (CSP) and cloud users. On contrary, private cloud is completely managed by the owned single cloud user/client.

The security responsibilities are also shared based on this setup. Particularly, the CSP is accountable to secure the shared cloud infrastructure. For more clarity, the CSP needs to focus on cloud API, storage network, routers, DNS, firewalls, switches, load balancers, directory services, and hypervisors.

By realizing the importance of cloud security, our research team passionately spent more time identifying recent research ideas in cloud security . Also, we have long-term practice in developing cloud security-based applications which are sure to meet the current security demand of the cloud system. Here, we have given some thought-provoking up-to-date research notions of cloud security.

PhD Research Topics in Cloud Computing Security

A major portion of cloud security planning and design. Since this helps to construct the trust of the cloud
Act as a security tool to manage the information of the cloud users from unauthorized access and attacks
Assure to pinpoint the security issues corresponding to fault tolerance, access/change control, business continuity plan, susceptibility analysis, incident reaction, backup, and disaster recovery
Users can employ or utilize the service regardless of their location and devices
To support remote access, the user only needs a strong internet connection, and devices like digital gadgets
Support sharing of resources and services over cloud environment. For instance: network and storage
Enable the user to access the resource from anywhere at any time through sophisticated cloud infrastructure
Need to support distributed cloud environment which executes the applications in multiple servers in a distant location
Provide security to the applications that communicate with external sources
Eliminate the unsuitable sensitive data access
CSP measure the usage of cloud-based resources, services, and software for billing the services
Cloud networks should have the ability to support a large volume of devices without affecting the actual system performance
The environment should be more flexible and scalable to adapt run-time changes
For example Facebook
Authenticate the user using their identity info like ID and password and Control their accessibility
For instance: Hand geometry, Fingerprint detection, Iris/ Retina Scan, Voice recognition, etc.
Security is more important in cloud computing. Since the application information will be shared among different clouds, So, the mobility of cloud data is need to be monitored and controlled in cloud computing
In general, it requires standard security technologies, infrastructure, data transport format, transmission approaches
Also, it is essential to assure data readiness, integrity, privacy while distributing the data over a heterogeneous network

Once the topic is confirmed, then we need to choose the development tool for implementing your research topic in reality. There are so many tools have designed for cloud computing. Each tool has different functionalities and working processes . Based on the project requirements, we need to select the best tool which gives accurate results.

IMPORTANT TOOLS FOR CLOUD COMPUTING SECURITY

Support researchers/developers to create large-scale cloud testbeds
Design and simulate the cloud computing models
Easy to monitor the cloud workloads and infrastructure through measuring devices, inspecting prototypes and network libraries
Through jetting protocols, it performs an operative process in a 10Gb/s network over the internet
Extension of cloudsim and cloudlet with scalable network
Support modeling of complex application/system
Self-control security verification and test tool
Communication among cloud and mobile networks
A network simulation tool to support analyze how the network process takes place
Measures the network behavior in cloud
Analyze the cloud features as SLA, VM migration, storage, working devices, network, algorithms, power usage, etc.
Open source programming software, language and analyzer
Design the transmission service between user and cloud
Also, it is a verification tool that analyze the designed model and authenticate all the entities in the model
Security protocol based verification tool for protecting cloud environs
Build the Elliptic Curve Integrated Encryption Scheme (ECIES) model
Design and simulate the security system based on BAN
Employ the AVISPA tool for formal security verification
Java-enabled simulator tool also called as Gr-Grid oud-Cloud
Used to model the cloud and grid applications/devices
Special characteristics to analyze the employed technology inheritance and value
Works effectively in the complicated simulation system
Mainly intended for IaaS applications along with its subordinates as TaaS, DaaS, and PaaS

At present, the above-specified tools play a significant role in developing cloud security-related systems. Beyond this, we also have other simulators which many of the scholars preferred to practically simulate their research ideas.

How to Write a Good Thesis Statement?

In the overall research phases, the thesis requires special attention because it gives valuable information about how you create and construct your research work. It describes all the activities of your research starting from topic selection to execution with accurate results. The first and foremost thing in the research process is identifying the interesting research area. From that, you have to select an innovative research idea . The title of the research will give the overall outline of your work. So, be aware while selecting the research topic.

For the best Research Topics in Cloud Computing Security , you can approach our team. We will let you know the research updates on recent technological developments. After finalizing the topic, identify the appropriate solutions for solving the handpicked problem. Then, we give you code implementation support through suitable development tools and technologies. At last, prepare the perfect master thesis to speak out your efforts in the research journey. For your information, our native writers have given some key aspects that enhance your thesis quality which produces a flawless thesis. Ultimately, it directs you and readers to do a study on future research on various themes. Such effective research helps like,

Complete the thesis statement with improved coherence
Justify a thesis statement and fulfill the thesis requirements in all the respects
Exhibit the flow of the research process in a well-structured manner with a complete explanation
Evaluate the obtained research outcome and discuss the fair conclusions with suitable experimental evidence
Make the examiners understand the proposed research work and their importance

On the whole, we give our step-by-step guidance in each phase of the research. Also, we let you know more ground-breaking Research Topics in Cloud Computing Security. So, make use of this opportunity and contact us to create remarkable research work.

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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

Tools for Green Cloud Computing

CloudSim 4.0
CloudAnalyst
GreenCloud 2.1.2
iCanCloud 1.0
EMUSIM, GroudSim
DCSim also with in Eclipse
NS-3 and also OMNeT++

We have to work upon all the above tools for cloud execution. Further, we also have a range of algorithms to greener the cloud . Each of them will aid you in achieving the best outcome in your PhD. Thus, you can find all-inclusive support for your PhD from us.

With us, you will get all you wish for your PhD…

Central Algorithms

Optimization algorithms (as GA, PSO, SMO, BFO, BSA and also ACO ) for task scheduling, resource allocation, and VM migration
Machine learning algorithms (like SVM, ANN, KNN, K-means, DT and also SOM ) for green data centers
Deep learning algorithms (such as DNN, DRL, DQN, DPN and also DBN ) for green cloud framework

Work with our keen thoughts to make the research your identity!!!

More relevant innovative research ideas from PhD research topics in green cloud computing,

An innovative mechanism for Forming and Revising of Cloud Infrastructures

Creative mechanism for Task-Centric Mobile Cloud-Based System in to Enable Energy-Aware Efficient Offloading scheme

An effective function for Locust-Inspired Scheduling Algorithm to Reduce Energy Consumption in Cloud Datacenters

The novel scheme for Fuzzy-based on Fog Computing intended for Real-Time Data Transmission in Healthcare Internet-of-Things

An effectual function for Energy-Aware VM Consolidation in Cloud Data Centers Using Utilization Prediction Model

The novel function for Energy-Aware VM Placement and Task Scheduling in Cloud-IoT Computing

A new-fangled mechanism for Green and Sustainable Cloud of Things

An inventive source for Design and implementation of a power consumption management system for smart home over fog-cloud computing

A new scheme for Exploiting Non-Causal CPU-State Information designed for Energy-Efficient Mobile Cooperative Computing

The new thing for Outline function based on Applications and Security Issues of Fog Computing

An imaginative function for Heterogeneity Aware Workload Management in Distributed Sustainable Datacenters

An innovative mechanism for Parallel-Batch Multi-Objective Job Scheduling Algorithm in Edge Computing

An innovative performance for Temporal Task Scheduling of Multiple Delay-Constrained Applications in Green Hybrid Cloud

The new process for Multi-Queue Scheduling of Heterogeneous Tasks with Bounded Response Time in Hybrid Green IaaS Clouds

An effective function for Commodity SBC-Edge Cluster for Smart Cities

An efficient mechanism based on Agricultural Data Gathering Platform by Internet of Things and Big Data

The new source for Distributed Robust Power Minimization for Downlink of Multi-Cloud Radio Access Networks

An innovative performance for Iot Based on Smart Shopping Mall system

An effective performance for General Approach For Patient Health Care Monitoring System Through IoT

The new mechanism for Semantic Multimedia Fog Computing and IoT Environment based on Sustainability Perspective

PhD Research Topics in Green Cloud Computing

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Phd Research Topics In Ubiquitous Computing

Phd Research Topics In Service Computing

Phd Research Topics In Grid Computing

Phd Research Topics In Soft Computing

Phd Research Topics In Green Network

Phd Research Topics In Sdn Cloud

Phd Research Topics In Load Balancing Cloud

Phd Research Topics In Information Technology

Phd Research Topics In Mobile Computing

Phd Research Topics In Internet Computing

Phd Research Topics In Pervasive Computing

Phd Research Topics In Social Sensor Networks

Phd Research Topics In Mobile Cloud Computing

Phd Research Topics In Green Cloud Simulator

Phd Research Topics In Software Defined Cloud Networking

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FINLAND: 100 PhD positions are available in the field of Artificial Intelligence

100 PhD positions in the field of Artificial Intelligence

The Finnish Doctoral Program Network in Artificial Intelligence is looking for 100 new PhD students to work in fundamental AI and machine learning research and in five application areas. Come do a PhD tackling challenging research questions in a network that fosters industry and multidisciplinary collaboration! T

About the program

The Finnish Doctoral Program Network in Artificial Intelligence launched in 2024 to build a world-class PhD program with quality supervision, mobility, and multi-disciplinarity as integral parts. The program is a joint effort of ten Finnish universities and will educate 100 new PhDs in artificial intelligence research. Finland’s Ministry of Education and Culture has granted 25.5 million EUR to support the program.

The PhD students joining the program will benefit from:

Ability to do fully-funded, curiosity-driven research with high-quality supervision from experienced researchers

Multidisciplinary environment with experts both in fundamental machine learning research as well as several application areas

Built-in collaboration opportunities with industry

Support for international mobility periods and links to top international partners, through e.g., ELLIS AI network of excellence

Possibility to attend summer schools, research seminars, workshops and networking events

Access to high-end infrastructure, career training and support services

Research areas

We are looking for prospective PhD students to work in the following research areas:

Fundamental AI

Fundamental AI methods are the core of the FCAI research activities and the cornerstone in all application areas. Fundamental AI encompasses probabilistic AI for verifiable and uncertainty-aware model building, simulation-based inference for efficient and interpretable reasoning capabilities, data-efficient deep learning, privacy-preserving and secure AI, interactive AI for collaborative AI tools, autonomous AI, statistics, and decision-making. Widely applicable goals of the fundamental AI are AI-assisted decision-making, design and modeling.

Keywords : Artificial Intelligence, Causal Inference, Collaborative AI and human modeling, Machine Learning, Statistics

AI in Communications and Signal Processing

The area covers a wide range of advanced methods in communications and distributed intelligence technologies, statistical methods in signal processing, and analysis of images, video, speech, audio and array signals.

The methodologies can be applied in various layers of communications systems from applications to the radio connectivity with distributed intelligence that is an integral part of next generation communication and computing systems targeting to solve issues related to ultra densification of infrastructure, devices and people, and to guarantee secure, low latency and reliable use of ICT resources using advanced AI methods.

This research area also includes acquiring, processing, analyzing and understanding digital images, video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, 3D point clouds from LiDaR sensors, or medical scanning devices, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions, using models constructed with the aid of geometry, physics, statistics, and learning theory.

Keywords : Array signal processing, Computer vision, Edge intelligence, Perception, Sensors, Wireless communications

AI in Health

The health and wellbeing field holds high potential to profit from advances in AI. Applications range from personalized care and precision medicine to preventive care and to process optimization. Increasing availability of large amounts of multi-source data combined with novel AI paradigms give huge opportunities. Challenges are how to extract valid actionable knowledge from all that data, how to develop AI-based solutions that are trustworthy, fit into healthcare processes, and that have an actual impact.

Keywords : Biomedical Image and Signal analysis; Multi-modal Health Data Analysis; Predictive, Preventive, Personalized, Participatory Healthcare, Trustworthy AI, Healthcare Processes.

AI in Engineering

Industries are currently employing AI methods in numerous research and development tasks. Examples include product design, predictive maintenance, and combining physical models with data-based methods. There is a great potential also in replacing laboratory development and experiments with virtual laboratory-type approaches. Research topics include:

AI methods in industrial research and development, including:

AI for product design and optimization, combining physic-based and data-driven models.

AI for improving industrial operations: cyber security, anomaly detection in industrial time series and predictive maintenance.

Methods supporting AI in industrial deployments, including on-device learning and federated learning on edge devices.

Virtual laboratories for experimentation and cost-effective product design and validation.

AI methods for autonomous functions in land, sea, air and space vehicles and machines. These range from pilot assistance, collision avoidance and navigation systems to full-mission autopilots.

Keywords : Autonomous systems, Energy systems, Machine automation, Manufacturing, Materials, Mechanical engineering, Robotics

AI in Language and Speech Technology

The area covers all aspects of natural language processing (NLP), a field of research dealing with computational analysis and generation of human language. NLP is a broad field which spans from highly technical research on machine learning techniques for written and spoken language data, through the myriad of individual tasks such as machine translation and information retrieval, to digital linguistics. The field is reliant on very large datasets and high performance computing, offering exciting software engineering and algorithmic challenges. Finland has a long tradition of top-notch NLP research, especially in the multilingual setting and, recently, large language model development.

Keywords : Foundation models, Human language technology, Natural Language Processing, NLP, Large language models, Speech recognition, Speech generation, Machine translation, Crosslingual model.

AI in Society and Business

The area examines the societal, ethical, and economic dimensions of AI, including trustworthy and societally acceptable AI as well as the consequences of the uses of AI. It brings together AI research with social sciences and humanities to gain in-depth understanding of AI’s role in organizations, society, business, and the economy. It includes uses of AI in education and education about AI. The area fosters interdisciplinarity to reinforce cross-cutting themes such as sustainability, ethics, equity, trust, and social responsibility.

Keywords : AI in business operations, AI in society, AI and Education, AI Ethics

What we offer

Research environment.

The doctoral program is hosted by the Finnish Center for Artificial Intelligence FCAI , an international research hub and one of the Research Council of Finland’s Flagships, hubs of top-level research and impact. FCAI’s research spearheads are ranked in the top-3 in Europe and in the top-30 globally and it’s built on a long track record of pioneering machine learning research in Finland. FCAI is also tightly-knit part of ELLIS , the pan-European AI network of excellence, hosting the local ELLIS Unit and coordinating ELISE , a European Network of Artificial Intelligence Excellence Centres.

Recruiting universities

The doctoral program has a broad range of possibilities to work with companies and academic partners. Jointly designed PhD topics and joint supervision (e.g., between research areas, universities, and together with industry) will provide PhD students with a large pool of expertise and guidance. Industrial collaboration is possible in all the research areas. Potential topics for industry collaboration include 1) AI for radio systems (e.g., 6G), network optimization, and cloud technologies, 2) AI for pharmaceutical development, imaging, and personalized medicine, 3) AI for smart systems, software development, cybersecurity, sensing, energy management, production processes, manufacturing, and predictive maintenance, 4) generative AI, LLMs, and speech technologies, and 5) trustworthy AI in public services, fintech and business operations. All students are offered entrepreneurship training, a connection to the local startup ecosystem, and access to a company fair to bridge post-PhD career options. Our international academic collaborations provide top-quality mobility possibilities, e.g., 6-month research exchanges and access to summer schools and workshops.

Potential collaborators include

The PhD students will have access to excellent computing facilities through our local and national computational services. CSC – IT Center for Science has partnered with the doctoral program, further facilitating our researchers’ access to high-end computing infrastructure, including Europe’s fastest supercomputer LUMI .

The doctoral program offers a flying start to the PhD studies by integrating the PhD topics into ongoing research and providing peer support and help in getting the first scientific paper out quickly. The program organizes summer schools and research seminars that further support learning from peers and building networks. We also offer help for international students to settle in Finland, e.g., with language courses and support with accessing practical information.

We are strongly committed to offering everyone an inclusive and non-discriminating working environment and warmly welcome qualified candidates from all backgrounds to apply and particularly encourage applications from women and other groups underrepresented in the field.

Job details

The positions are based at one of the ten universities that are part of the Finnish Doctoral Program Network in Artificial Intelligence. The recruiting university will be the same as that of the primary supervisor. The matching of the candidates with supervisors will be done during the review process and the candidates will have a chance to prioritise the supervising professor they want to work with (see details in FAQ ).

All positions are fully-funded. PhD student contracts will be made for three years. The terms of employment and the salaries are based on the General Collective Agreement for Universities . The contract includes occupational healthcare.

We are looking for 100 new PhD students in two calls (in spring and fall 2024). The accepted candidates of the spring call are expected to start in August 2024, and the applicants from the fall call in January 2025.

What we look for

Successful candidates should have previous experience in machine learning, statistics, artificial intelligence, in another relevant field, demonstrated by success in related studies and ideally also by some publication record. For the candidates applying for positions in applied research areas, relevant experience and expertise in the application area are valued in the evaluation. Other merits demonstrating suitability for a researcher position can also be considered. Candidates should hold (or shortly receive) a Master’s degree in computer science, statistics, electrical engineering, mathematics, relevant application area or in another relevant field. The degree should preferably be completed before the start of the employment.

The positions require the ability to work both independently and as part of a team in a highly collaborative and interdisciplinary environment. The primary working language in the joint program activities is English, so good written and oral command of English is required (see details in FAQ ).

Formal requirements

Candidates accepted in the doctoral program will need to apply for the study right for doctoral studies at the university where they will be based. Depending on the partner university where the position is based, the candidate will either need to have the study right prior to recruitment, or get the study right within the probation period of the first 6 months. The requirements for the study right can differ slightly between the universities, but the general prerequisites are:

Master’s degree in a relevant field (completed by the time of applying for the study right)

Proficiency in English, Finnish, or Swedish (typically demonstrated with an official certificate, e.g., IELTS/TOEFL)

Please see FAQ (question 7) for university-specific requirements.

How to apply

We are looking for 100 new PhD students to join the Finnish Doctoral Program Network in Artificial Intelligence in two calls: the first one is open March 11–April 2, 2024 and the second will open in fall 2024.

Candidates will apply to all universities and application areas with the same joint application. In the application form, you are able to indicate which specific research areas and supervisors you are interested in. Note: Candidates who apply to supervisors based at the University of Helsinki, will have to submit a parallel application to the university’s own recruitment system. Please note that the application needs to be submitted to both of the recruitment systems to ensure a proper review. See further details .

The deadline for applications in the ongoing call is April 2, 2024 . Please submit your application in our online recruitment system with the required attachments (detailed below).

Required attachments:

Motivation letter (1–2 pages). Please specify the research area(s) and preferably the supervisors with whom you want to work.

List of publications (if relevant; please do not attach full copies of publications)

A transcript of master’s/bachelor’s studies and the degree certificate of your latest degree. If you don’t have a Master's degree, a plan of completion must be submitted.

In the application form, you are also asked to provide contact details of two senior academics who can provide references.

All materials should be submitted in English in a PDF format. Note: You can upload max. five files to the recruitment system, each max. 5MB.

DEADLINE: April 2, 2024

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AI generates high-quality images 30 times faster in a single step

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Three by two grid of AI-generated images, with small black illustrated robots peeking from behind. The images show a scenic mountain range; a unicorn in a forest; a vintage Porsche; an astronaut riding a camel in a desert; a sloth holding a cup, dressed in a turtleneck sweater; and a red fox in a spacesuit against a starry background.

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In our current age of artificial intelligence, computers can generate their own “art” by way of diffusion models , iteratively adding structure to a noisy initial state until a clear image or video emerges. Diffusion models have suddenly grabbed a seat at everyone’s table: Enter a few words and experience instantaneous, dopamine-spiking dreamscapes at the intersection of reality and fantasy. Behind the scenes, it involves a complex, time-intensive process requiring numerous iterations for the algorithm to perfect the image.

MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have introduced a new framework that simplifies the multi-step process of traditional diffusion models into a single step, addressing previous limitations. This is done through a type of teacher-student model: teaching a new computer model to mimic the behavior of more complicated, original models that generate images. The approach, known as distribution matching distillation (DMD), retains the quality of the generated images and allows for much faster generation.

“Our work is a novel method that accelerates current diffusion models such as Stable Diffusion and DALLE-3 by 30 times,” says Tianwei Yin, an MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and the lead researcher on the DMD framework. “This advancement not only significantly reduces computational time but also retains, if not surpasses, the quality of the generated visual content. Theoretically, the approach marries the principles of generative adversarial networks (GANs) with those of diffusion models, achieving visual content generation in a single step — a stark contrast to the hundred steps of iterative refinement required by current diffusion models. It could potentially be a new generative modeling method that excels in speed and quality.”

This single-step diffusion model could enhance design tools, enabling quicker content creation and potentially supporting advancements in drug discovery and 3D modeling, where promptness and efficacy are key.

Distribution dreams

DMD cleverly has two components. First, it uses a regression loss, which anchors the mapping to ensure a coarse organization of the space of images to make training more stable. Next, it uses a distribution matching loss, which ensures that the probability to generate a given image with the student model corresponds to its real-world occurrence frequency. To do this, it leverages two diffusion models that act as guides, helping the system understand the difference between real and generated images and making training the speedy one-step generator possible.

The system achieves faster generation by training a new network to minimize the distribution divergence between its generated images and those from the training dataset used by traditional diffusion models. “Our key insight is to approximate gradients that guide the improvement of the new model using two diffusion models,” says Yin. “In this way, we distill the knowledge of the original, more complex model into the simpler, faster one, while bypassing the notorious instability and mode collapse issues in GANs.”

Yin and colleagues used pre-trained networks for the new student model, simplifying the process. By copying and fine-tuning parameters from the original models, the team achieved fast training convergence of the new model, which is capable of producing high-quality images with the same architectural foundation. “This enables combining with other system optimizations based on the original architecture to further accelerate the creation process,” adds Yin.

When put to the test against the usual methods, using a wide range of benchmarks, DMD showed consistent performance. On the popular benchmark of generating images based on specific classes on ImageNet, DMD is the first one-step diffusion technique that churns out pictures pretty much on par with those from the original, more complex models, rocking a super-close Fréchet inception distance (FID) score of just 0.3, which is impressive, since FID is all about judging the quality and diversity of generated images. Furthermore, DMD excels in industrial-scale text-to-image generation and achieves state-of-the-art one-step generation performance. There's still a slight quality gap when tackling trickier text-to-image applications, suggesting there's a bit of room for improvement down the line.

Additionally, the performance of the DMD-generated images is intrinsically linked to the capabilities of the teacher model used during the distillation process. In the current form, which uses Stable Diffusion v1.5 as the teacher model, the student inherits limitations such as rendering detailed depictions of text and small faces, suggesting that DMD-generated images could be further enhanced by more advanced teacher models.

“Decreasing the number of iterations has been the Holy Grail in diffusion models since their inception,” says Fredo Durand, MIT professor of electrical engineering and computer science, CSAIL principal investigator, and a lead author on the paper. “We are very excited to finally enable single-step image generation, which will dramatically reduce compute costs and accelerate the process.”

“Finally, a paper that successfully combines the versatility and high visual quality of diffusion models with the real-time performance of GANs,” says Alexei Efros, a professor of electrical engineering and computer science at the University of California at Berkeley who was not involved in this study. “I expect this work to open up fantastic possibilities for high-quality real-time visual editing.”

Yin and Durand’s fellow authors are MIT electrical engineering and computer science professor and CSAIL principal investigator William T. Freeman, as well as Adobe research scientists Michaël Gharbi SM '15, PhD '18; Richard Zhang; Eli Shechtman; and Taesung Park. Their work was supported, in part, by U.S. National Science Foundation grants (including one for the Institute for Artificial Intelligence and Fundamental Interactions), the Singapore Defense Science and Technology Agency, and by funding from Gwangju Institute of Science and Technology and Amazon. Their work will be presented at the Conference on Computer Vision and Pattern Recognition in June.

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AI generates high-quality images 30 times faster in a single step
In our current age of artificial intelligence, computers can generate their own "art" by way of diffusion models, iteratively adding structure to a noisy initial state until a clear image or video emerges.Diffusion models have suddenly grabbed a seat at everyone's table: Enter a few words and experience instantaneous, dopamine-spiking dreamscapes at the intersection of reality and fantasy.

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