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This study presents a cloud computing approach that leverages the latest trends in the contemporary IT sector. The significance of data storage and utilization is steadily growing. The increasing magnitude of data has been a significant obstacle in terms of storage capacity. In certain locations, there exists an abundance or underutilization of resources, but in other areas, these resources are rare or unavailable. The establishment of the cloud computing business was intended to address the issue of imbalanced resource consumption within this society. This proposal offers a potential resolution for a societal context characterized by unequal distribution of resources. However, challenges related to resource allocation, task fulfillment, and ensuring quality of service in service provision continue to persist. The Modified Genetic Particle Swarm Optimization (MGPSO) technique enhances the efficiency of resource allocation and enhances the level of service provided. With the objective of optimizing resource use, this algorithm effectively arranges tasks that users submit to the resource.

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Evaluation of the MGPSO’s Optimal Resource Distribution for Achieving Cost Effective Cloud Computing with Genetic Algorithm Particle Swarm Optimization (GA PSO)

  • 会议论文

  • 发表日期：

    2025年

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  • Sayyad Rasheeduddin,
  • V. Sravani Kumari,
  • L. Chandra Sekhar Reddy,
  • Maddela Parameswar,
  • Mahesh Kotha,
  • Raj Kumar Patra

摘要

This study presents a cloud computing approach that leverages the latest trends in the contemporary IT sector. The significance of data storage and utilization is steadily growing. The increasing magnitude of data has been a significant obstacle in terms of storage capacity. In certain locations, there exists an abundance or underutilization of resources, but in other areas, these resources are rare or unavailable. The establishment of the cloud computing business was intended to address the issue of imbalanced resource consumption within this society. This proposal offers a potential resolution for a societal context characterized by unequal distribution of resources. However, challenges related to resource allocation, task fulfillment, and ensuring quality of service in service provision continue to persist. The Modified Genetic Particle Swarm Optimization (MGPSO) technique enhances the efficiency of resource allocation and enhances the level of service provided. With the objective of optimizing resource use, this algorithm effectively arranges tasks that users submit to the resource.