Optimizing Cloud Resource Management Using PSO


  • Anshika Rawat Amity School of Engineering and Technology Lucknow, Amity University Uttar Pradesh, India
  • Dr. P. Singh Amity School of Engineering and Technology Lucknow, Amity University Uttar Pradesh, India
  • Shubham Singh Department of Computer Science and Engineering, GLA University, Mathura, Uttar Pradesh, India




Fire Fly algorithm, Genetic Algorithm, Cloud Computing, PSO, Aws


This research explores how cloud resource management is changing in businesses, with a focus on Amazon Web Services (AWS) as the leader in cloud computing. It highlights how crucial excellent resource management is to attaining scalability, cost-effectiveness, and peak performance. The study explores on using Particle Swarm Optimization (PSO) as a cutting-edge optimization method in cloud computing settings. It talks about the difficulties brought on by fluctuating workloads and the requirement for clever resource allocation strategies. Additionally, the study assesses several optimization techniques using performance parameters including computing overhead, convergence time, and solution quality. These techniques include PSO, Genetic Algorithm (GA), and Firefly Algorithm (FA). In-depth simulations and case studies with organizations such as Siemens and Deloitte are used in the study to demonstrate how these algorithms work best in cloud environments to maximize resource usage, cut costs, and improve overall service quality. In the end, it emphasizes the continuous requirement for optimizing techniques to successfully handle the complexity of cloud computing ecosystems.


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How to Cite

Anshika Rawat, P. Singh, and Shubham Singh, “Optimizing Cloud Resource Management Using PSO”, J. Infor. Electr. Electron. Eng., vol. 5, no. 1, pp. 1–17, Apr. 2024.




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