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Research on Task Scheduling Based on Particle Swarm Optimization Simulated Annealing Algorithm in Hybrid Cloud Environment

Published: 29 October 2021 Publication History

Abstract

In recent years, cloud computing has developed rapidly. Some problems exist in traditional schedule, such as inefficient task management and unreasonable resource allocation. To solve these problems, a particle swarm simulated annealing (PSO-SA) algorithm is proposed, which improves the inertia weight and learning factor, and redefines the adaptability function, thus effectively improving the task management in the hybrid cloud environment, and further improving the rational allocation of resources. The simulation experiments on the number of tasks and resources show that the performance of PSO-SA algorithm is enhanced after optimization.

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  • (2023)Research on Optimization of Resource Scheduling Algorithm Based on Responsive Web Front-EndAdvances in Communication, Devices and Networking10.1007/978-981-99-1983-3_40(441-449)Online publication date: 8-Jul-2023

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cover image ACM Other conferences
ICIIP '21: Proceedings of the 6th International Conference on Intelligent Information Processing
July 2021
347 pages
ISBN:9781450390637
DOI:10.1145/3480571
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2021

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Author Tags

  1. Cloud computing
  2. Particle swarm optimization algorithm
  3. Simulated annealing algorithm
  4. Task scheduling

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ICIIP 2021

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Overall Acceptance Rate 87 of 367 submissions, 24%

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  • (2023)Research on Optimization of Resource Scheduling Algorithm Based on Responsive Web Front-EndAdvances in Communication, Devices and Networking10.1007/978-981-99-1983-3_40(441-449)Online publication date: 8-Jul-2023

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