A modified PSO algorithm for task scheduling optimization in cloud computing. (21st September 2018)

• Record Type: Journal Article
• Authors: Zhou, Zhou; Chang, Jian; Hu, Zhigang; Yu, Junyang; Li, Fangmin
• Other Names: Laccetti Giuliano guestEditor.; Lapegna Marco guestEditor.; Montella Raffaele guestEditor.; Kosta Sokol guestEditor.; Wu Shaofei guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 24(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-09-21
• Subjects: cloud computing -- inertia weight -- particle swarm optimization -- task scheduling; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4970
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: With the increasing scale of tasks in cloud computing, the problem of high energy consumption becomes increasingly serious. To deal with the problem, we propose a cloud computing energy consumption model, which takes into account the execution and transmission cost of the processor. Then, based on this model, we put forward a task scheduling optimization algorithm named modified particle swarm optimization (M‐PSO) to handle the local optimum and slow convergence problem. Different from the PSO, M‐PSO can dynamically adjust the inertia weight coefficient to improve the speed of convergence according to the number of iterations. Finally, the performance of the proposed algorithm is evaluated through the CloudSim toolkit, and the experimental results show that the M‐PSO can efficiently reduce total cost compared with other algorithms.