Cloud Computing Research Topics

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In contemporary years, there are several research topics that are progressing in cloud computing. The research topics that hot in today’s research world are shared below, get best experts assistance from phdtopic.com. Together with short literature review, the following are few of the major research topics in cloud computing:

  1. Cloud Security and Privacy

Literature Review:

  • Encryption Techniques: To protect data at inactive state and during transmission, study has concentrated on improving encryption techniques. The most prominent approaches are attribute-related encryption and homomorphic encryption, that contains the capability to permit data to be processed without being decrypted.
  • References: Gentry, C. (2009). Fully Homomorphic Encryption Using Ideal Lattices. STOC; Sahai, A., & Waters, B. (2005). Fuzzy Identity-Based Encryption. EUROCRYPT.
  • Intrusion Detection Systems (IDS): With the intention of enhancing the identification and reaction to protection attacks, create complicated IDS for cloud platforms through the utilization of machine learning and AI.
  • References: Modi, C., Patel, D., Borisaniya, B., Patel, A., & Rajarajan, M. (2013). A survey of intrusion detection techniques in the cloud. Journal of Network and Computer Applications, 36(1), 42-57.
  • Privacy-Preserving Data Mining: For facilitating confidentiality-preserving data analytics in cloud platforms, employ approaches such as secure multi-party computation and differential privacy.
  • References: Dwork, C. (2006). Differential Privacy. ICALP; Lindell, Y., & Pinkas, B. (2009). Secure multiparty computation for privacy-preserving data mining. Journal of Privacy and Confidentiality.
  1. Resource Management and Optimization

Literature Review:

  • Auto-Scaling Algorithms: Specifically, to facilitate dynamic scaling of cloud resources on the basis of actual-time requirement, different methods such as rule-based, threshold-based, and predictive scaling techniques have been constructed.
  • References: Lorido-Botran, T., Miguel-Alonso, J., & Lozano, J. A. (2014). A review of auto-scaling techniques for elastic applications in cloud environments. Journal of Grid Computing, 12(4), 559-592.
  • Load Balancing: In order to improve consistency and effectiveness, intend to disseminate workloads equally among servers through the utilization of approaches like least connections, round-robin, and AI-related techniques.
  • References: Chaczko, Z., Mahadevan, V., Aslanzadeh, S., & Mcdermid, C. (2011). Availability and load balancing in cloud computing. International Conference on Computer and Software Modeling, 14(5), 134-140.
  • Energy Efficiency: By means of methods such as dynamic voltage and frequency scaling (DVFS) and workload consolidation, this study concentrates on decreasing the energy utilization of cloud data centers.
  • References: Beloglazov, A., Buyya, R., Lee, Y. C., & Zomaya, A. Y. (2011). A taxonomy and survey of energy-efficient data centers and cloud computing systems. Advances in Computers, 82, 47-111.
  1. Big Data and Cloud Computing

Literature Review:

  • Big Data Processing Frameworks: Generally, scalable and effective processing of extensive datasets are facilitated by the combination of big data models such as Spark and Hadoop with cloud environments.
  • References: Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113; Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. HotCloud, 10(10-10), 95.
  • Real-Time Data Analytics: Concentrating on low-latency processing and stream analytics in cloud platforms, explore methods for actual-time data analytics.
  • References: Hirzel, M., Soulé, R., Schneider, S., Gedik, B., & Grimm, R. (2014). A catalog of stream processing optimizations. ACM Computing Surveys (CSUR), 46(4), 1-34.
  • Data Storage and Management: The study investigates fault-tolerant and scalable data storage approaches, in addition to NoSQL databases and distributed file systems.
  • References: Shvachko, K., Kuang, H., Radia, S., & Chansler, R. (2010). The Hadoop distributed file system. 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), 1-10.
  1. Edge and Fog Computing

Literature Review:

  • Edge-Fog-Cloud Architecture: To enhance actual-time data processing and decrease delay, this research concentrates on the combination of fog and edge computing with cloud architecture.
  • References: Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637-646.
  • Resource Management in Edge Computing: Intending to minimize delay and enhance effectiveness in fog and edge platforms, employ methods for task offloading and resource allocation.
  • References: Mahmud, R., Koch, F. L., & Buyya, R. (2018). Cloud-Fog Interoperability in IoT-enabled Healthcare Solutions. Proceedings of the 19th International Conference on Distributed Computing and Networking, 1-10.
  • Security and Privacy: Encompassing secure interaction and data security, solve the novel safety and confidentiality limitations in fog and edge computing.
  • References: Roman, R., Lopez, J., & Mambo, M. (2018). Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, 78, 680-698.
  1. Multi-Cloud and Hybrid Cloud Solutions

Literature Review:

  • Multi-Cloud Management: For solving interoperability and consistent combination, concentrate on handling resources among numerous cloud suppliers by investigating on architectures and tools.
  • References: Bernstein, D., Ludvigson, E., Sankar, K., Diamond, S., & Morrow, M. (2009). Blueprint for the Intercloud – Protocols and Formats for Cloud Computing Interoperability. Proceedings of the 4th International Conference on Internet and Web Applications and Services, 328-336.
  • Hybrid Cloud Architectures: For adaptability and cost-efficiency, integrate public and private cloud sources by investigating the structure and deployment of hybrid cloud approaches.
  • References: Buyya, R., Ranjan, R., & Calheiros, R. N. (2010). InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services. Algorithms and Architectures for Parallel Processing, 13-31.
  • Data Portability and Migration: Typically, for assuring data mobility and consistent movement among various cloud platforms, focus on employing suitable approaches.
  • References: Petcu, D. (2011). Multi-Cloud: Expectations and current approaches. Proceedings of the 2011 International Conference on High Performance Computing & Simulation, 340-347.
  1. Serverless Computing

Literature Review:

  • Function-as-a-Service (FaaS): To enhance scalability, decrease delay, and improve developer efficiency, investigate in enhancing serverless infrastructures.
  • References: Jonas, E., Schleier-Smith, J., Sreekanti, V., Tsai, C. C., Khandelwal, A., Pu, Q., & others (2019). Cloud programming simplified: A Berkeley view on serverless computing. arXiv preprint arXiv:1902.03383.
  • Cold Start Latency: With the aim of assuring rapid execution times, reduce cold start latency in serverless operations by making use of suitable methods.
  • References: Wang, L., Zhang, Y., & Ristenpart, T. (2018). Peeking behind the curtains of serverless platforms. Proceedings of the 2018 USENIX Annual Technical Conference (USENIX ATC 18), 133-146.
  • Cost Optimization: Through reducing execution time and resource utility, improve the expense of serverless computing by exploring policies.
  • References: Adzic, G., & Chatley, R. (2017). Serverless computing: economic and architectural impact. Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, 884-889.

What are the current research topics of virtualization or cloud computing?

In the domain of virtualization and cloud computing, there exists numerous research topics. We offer few recent research topics with the significant research areas and references:

Virtualization Research Topics

  1. Containerization and Microservices
  • Explanation: To enhance application scalability, implementation, and management, explore the purpose of microservices and containers.
  • Significant Areas: Performance improvement, container arrangement such as Kubernetes, protection in containerized platforms.
  • References: Merkel, D. (2014). Docker: lightweight Linux containers for consistent development and deployment. Linux Journal, 2014(239), 2.
  1. Virtual Machine (VM) Optimization
  • Explanation: Concentrating on resource allotment, planning, and overhead mitigation, it is appreciable to improve the effectiveness and efficacy of VMs.
  • Significant Areas: Hypervisor effectiveness, VM live migration, resource allocation methods.
  • References: Clark, C., Fraser, K., Hand, S., Hansen, J. G., Jul, E., Limpach, C., Pratt, I., & Warfield, A. (2005). Live migration of virtual machines. Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation – Volume 2, 273-286.
  1. Security in Virtualized Environments
  • Explanation: To secure virtualized platforms from assaults like inter-VM interaction susceptibilities, VM issues, and hypervisor threats, aim to construct safety technologies.
  • Significant Areas: Hypervisor safety, isolation approaches, safe VM introspection.
  • References: Azmandian, F., Chonka, A., Zhou, W., & Xiang, Y. (2012). Cloud security defense to protect cloud computing against HTTP-DoS and XML-DoS attacks. Journal of Network and Computer Applications, 34(4), 1097-1107.
  1. Network Function Virtualization (NFV)
  • Explanation: In order to enhance the scalability and adaptability of network services, investigate the virtualization of network operations.
  • Significant Areas: NFV arrangement, NFV infrastructure, performance improvement.
  • References: Han, B., Gopalakrishnan, V., Ji, L., & Lee, S. (2015). Network function virtualization: Challenges and opportunities for innovations. IEEE Communications Magazine, 53(2), 90-97.
  1. Energy Efficiency in Virtualized Data Centers
  • Explanation: By means of effective resource management and planning, explore on decreasing the energy utilization of virtualized data centers.
  • Significant Areas: Green computing, dynamic voltage and frequency scaling (DVFS), workload consolidation.
  • References: Beloglazov, A., & Buyya, R. (2012). Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience, 24(13), 1397-1420.

Cloud Computing Research Topics

  1. Edge and Fog Computing Integration
  • Explanation: To enhance actual-time data processing and decrease delay, improve the interaction among fog, cloud, and edge computing.
  • Significant Areas: Resource management, confidentiality, edge-fog-cloud infrastructure, and protection.
  • References: Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637-646.
  1. Serverless Computing
  • Explanation: With the intention to mitigate delay, increase developer efficiency, and enhance scalability, investigate on improving serverless infrastructure.
  • Significant Areas: Serverless safety, Function-as-a-Service (FaaS), cold start latency.
  • References: Jonas, E., Schleier-Smith, J., Sreekanti, V., Tsai, C. C., Khandelwal, A., Pu, Q., & others (2019). Cloud programming simplified: A Berkeley view on serverless computing. arXiv preprint arXiv:1902.03383.
  1. Multi-Cloud and Hybrid Cloud Solutions
  • Explanation: For effective management and interoperability among numerous cloud suppliers, develop suitable models and policies.
  • Significant Areas: Hybrid cloud combination, multi-cloud arrangement, data mobility and synchronization.
  • References: Petcu, D. (2011). Multi-Cloud: Expectations and current approaches. Proceedings of the 2011 International Conference on High Performance Computing & Simulation, 340-347.
  1. AI-Driven Cloud Optimization
  • Explanation: Specifically, to enhance cloud resource management, effectiveness, and energy efficacy, it is beneficial to utilize machine learning and artificial intelligence.
  • Significant Areas: Anomaly identification and reaction, predictive analytics for resource allotment, AI-based auto-scaling.
  • References: Lorido-Botran, T., Miguel-Alonso, J., & Lozano, J. A. (2014). A review of auto-scaling techniques for elastic applications in cloud environments. Journal of Grid Computing, 12(4), 559-592.
  1. Cloud Security and Privacy
  • Explanation: Typically, strong confidentiality and safety models have to be constructed to secure data and applications in cloud platforms.
  • Significant Areas: Zero-trust safety frameworks, secure multi-party computation, homomorphic encryption, AI-related intrusion detection systems.
  • References: Dwork, C. (2006). Differential Privacy. ICALP; Lindell, Y., & Pinkas, B. (2009). Secure multiparty computation for privacy-preserving data mining. Journal of Privacy and Confidentiality.
  1. Big Data and Cloud Computing
  • Explanation: In the cloud, focus on improving big data processing and analytics abilities in order to manage extensive datasets.
  • Significant Areas: Scalable big data models, AI for big data, actual-time analytics, data lakes and warehouses.
  • References: Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113; Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. HotCloud, 10(10-10), 95.
  1. Blockchain and Cloud Integration
  • Explanation: To improve cloud protection, clearness, and decentralized applications, utilize blockchain mechanism.
  • Significant Areas: Decentralized storage approaches, smart contract combination, blockchain-related cloud protection.
  • References: Christidis, K., & Devetsikiotis, M. (2016). Blockchains and smart contracts for the internet of things. IEEE Access, 4, 2292-2303.
  1. Quantum Computing and Cloud
  • Explanation: Mainly, to strengthen computational abilities, investigate the combination of quantum computing with cloud environments.
  • Significant Areas: Quantum encryption and protection, quantum cloud services, hybrid quantum-classical methods.
  • References: Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79.

Cloud Computing Research Projects

Cloud Computing Research Ideas

Choosing a thesis topic in cloud computing can be a daunting task for research students, often leading to mistakes. At phdtopic.com, we understand the challenges and strive to provide personalized assistance in selecting the perfect topic for your research document. A few of the Cloud Computing Research Ideas are discussed below. If you want to learn more about our services, feel free to reach out to us at phdtopic.com.

  1. Circumstantial Discussion on Security and Privacy Protection using Cloud Computing Technology
  2. A Review on Distributed Application Processing Frameworks in Smart Mobile Devices for Mobile Cloud Computing
  3. Resource Allocation Based on Double Auction for Cloud Computing System
  4. Performance Optimized routing for SLA enforcement in cloud computing
  5. A Critical Review Analysis of the Opportunities and Potential of Implementing Cloud Computing System for Large Scale Ad Hoc Network
  6. Simulating communication processes in energy-efficient cloud computing systems
  7. DLECP: A Dynamic Learning-based Edge Cloud Placement Framework for Mobile Cloud Computing
  8. Monitoring Performance in Large Scale Computing Clouds with Passive Benchmarking
  9. An efficient and robust one-time message authentication code scheme using feature extraction of iris in cloud computing
  10. The Glasgow Raspberry Pi Cloud: A Scale Model for Cloud Computing Infrastructures
  11. Cloud Computing on Cooperative Cars (C4S): An Architecture to Support Navigation-as-a-Service
  12. The method of ensuring confidentiality and integrity data in cloud computing
  13. Research on service-oriented cloud computing information security mechanism
  14. Integration of Cloud Computing and Big Data Technology for Smart Generation
  15. New Instructional Models for Building Effective Curricula on Cloud Computing Technologies and Engineering
  16. Performance analysis of cloud computing services for many-tasks scientific computing
  17. BlueSky cloud framework: an e-learning framework embracing cloud computing
  18. Opportunities and challenges of cloud computing to improve health care services
  19. Preparing for the future: understanding the seven capabilities cloud computing.
  20. A comparative study of the current cloud computing technologies and offers