Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (10): 291-298.doi: 10.11896/jsjkx.220800039

• Computer Network • Previous Articles     Next Articles

Reliability Constraint-oriented Workflow Scheduling Strategy in Cloud Environment

LI Jinliang1,2, LIN Bing2,3, CHEN Xing1,2   

  1. 1 College of Computer and Data Science,Fuzhou University,Fuzhou 350108,China
    2 Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou 350108,China
    3 College of Physics and Energy,Fujian Normal University,Fuzhou 350117,China
  • Received:2022-08-03 Revised:2022-12-24 Online:2023-10-10 Published:2023-10-10
  • About author:LI Jinliang,born in 1998,postgraduate,is a member of China Computer Federation.His main research interests include cloud computing and heuristic algorithms.LIN Bing,born in 1986,Ph.D,associate professor,postgraduate supervisor,is a member of China Computer Federation.His main research interests include cloud computing and intelligent computing and its application.
  • Supported by:
    National Natural Science Foundation of China(62072108),Natural Science Foundation of Fujian Province for Distinguished Young Scholars(2020J06014) and Fujian University Industry-University Cooperation Project(2022H6024).

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Abstract: As more and more computationally intensive dependent applications are offloaded to the cloud environment for execution,the problem of workflow scheduling has received extensive attention.Aiming at the workflow scheduling problem of multi-objective optimization in cloud environment,and considering that the server may experience performance fluctuations and downtime during task execution,based on fuzzy theory,a triangular fuzzy number is used to represent task execution time and data transmission time.A genetic algorithm-based adaptive particle swarm optimization based GA(APSOGA) is proposed.The purpose is to comprehensively optimize the completion time and execution cost of the workflow under the reliability constraints of the workflow.In order to avoid the premature convergence problem of the traditional particle swarm optimization algorithm,the proposed algorithm introduces the random two-point crossover operation and single-point mutation operation of the genetic algorithm,which effectively improves the search performance of the algorithm.Experimental results show that,compared with other strategies,APSOGA-based scheduling strategy can effectively reduce the time and cost of reliability-constrained scientific workflows in cloud environments.

Key words: Cloud computing, Reliability constraints, Uncertainty, Multi-objective optimization, Triangular fuzzy number

CLC Number: 

  • TP393
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