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Research on cloud computing task scheduling based on improved evolutionary algorithm

Published: 24 March 2021 Publication History

Abstract

In the research of cloud computing, the advantages and disadvantages of cloud task scheduling algorithm will affect the operation efficiency and service quality of the whole cloud computing system. Evolutionary algorithm is the sum of a series of specific algorithms inspired by the phenomenon of biological evolution in nature. One of the common points of these algorithms is that individuals must be mutated according to certain rules in the running process, so as to avoid falling into local optimum. In order to improve the efficiency of cloud task scheduling in cloud computing, this paper proposes a new mutation strategy which changes the genetic algorithm in evolutionary algorithm. It uses cloudsim platform to simulate cloud task scheduling in cloud computing, and uses particle swarm optimization algorithm to optimize its parameters. The experimental results show that the proposed evolutionary algorithm with improved mutation strategy has the function of cloud task scheduling, and its performance is also improved after the parameters are optimized by particle swarm optimization algorithm. The proposed algorithm improves the mutation step and explores the essence of mutation in evolutionary algorithm, which provides a reference for other research.

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  • (2022)Research on task scheduling based on improved particle swarm optimization in cloud computing environmentSecond International Conference on Digital Signal and Computer Communications (DSCC 2022)10.1117/12.2641288(19)Online publication date: 4-Aug-2022

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EBIMCS '20: Proceedings of the 2020 3rd International Conference on E-Business, Information Management and Computer Science
December 2020
718 pages
ISBN:9781450389099
DOI:10.1145/3453187
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]

In-Cooperation

  • Guilin: Guilin University of Technology, Guilin, China
  • International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2021

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Author Tags

  1. Cloud computing
  2. Cloudsim
  3. Evolutionary algorithm
  4. Particle swarm optimization algorithm
  5. Task scheduling

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  • Refereed limited

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EBIMCS 2020

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EBIMCS '20 Paper Acceptance Rate 112 of 566 submissions, 20%;
Overall Acceptance Rate 143 of 708 submissions, 20%

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  • (2022)Research on task scheduling based on improved particle swarm optimization in cloud computing environmentSecond International Conference on Digital Signal and Computer Communications (DSCC 2022)10.1117/12.2641288(19)Online publication date: 4-Aug-2022

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