|Table of Contents|

A Cloud Computing Task Scheduling Algorithm Based on Improved Artificial Fish Warm(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2024年01期
Page:
91-102
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
A Cloud Computing Task Scheduling Algorithm Based on Improved Artificial Fish Warm
Author(s):
Sun Jian1; 2; Wu Zhuiwei1; Liu Chenwei1; Wu Tao1
(1.School of Computer Science and Engineering,North Minzu University,Yinchuan 750021,China)
(2.The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission,North Minzu University,Yinchuan 750021,China)
Keywords:
cloud computing; task scheduling; AFSA; CloudSim; makespan; cost
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2024.01.011
Abstract:
In order to improve the efficiency of cloud computing task scheduling and reduce the makespan and cost of tasks,this paper proposes an improved artificial fish swarm task scheduling algorithm(IAFSA). Firstly,the opposition-based learning strategy was applied to the population initialization and the behavior selection of the fish swarm to improve the convergence speed and population diversity of the improved artificial fish swarm algorithm in iterations. Secondly,the adaptive global-local memory mechanism was introduced into the foraging behavior of the standard AFSA algorithm to further improve the exploration ability. Finally,an action selection mechanism based on average fitness was added to provide more reasonable action selection and reduce the complexity of the algorithm. By using CloudSim platform for experimental verification,the algorithm efficiency of IAFSA under different task scales was tested respectively. The experimental results show that the improved artificial fish swarm algorithm has significant advantages in reducing the maximum completion time and cost of the system task.

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Last Update: 2024-03-15