Title: A memory-based task scheduling algorithm for grid computing based on heterogeneous platform and homogeneous tasks

Authors: Kunhao Tang; Wei Jiang; Ruonan Cui; Youlong Wu

Addresses: Department of Computer and Information Science, Hunan Institute of Technology School, China ' Department of Computer and Information Science, Hunan Institute of Technology, China ' School of Economics and Management, Hunan Institute of Technology, China ' Department of Computer and Information Science, Hunan Institute of Technology, China

Abstract: Grid computing is a new computing mode in recent years, which focuses on parallel infrastructure and its comprehensive application ability to network computers and distributed processors. Grid computing has been fully applied in the field of modern information technology and computer. Task scheduling is the core of grid computing. The quality of task scheduling algorithm directly affects the response time of the whole computing system. For heterogeneous tasks on heterogeneous platforms, this paper proposes a task scheduling algorithm with memory function, and introduces the distributed particle swarm optimisation algorithm into this algorithm, which realises the combination of resource processing tasks in grid computing and the behaviour characteristics of intelligent groups, so as to better realise the dynamic and scalable scheduling of heterogeneous tasks on heterogeneous platforms to adapt to grid environment sex. Finally, the grid simulation software GridSim is used to simulate the algorithm proposed in this paper. At the same time, it is compared with the state stochastic scheduling algorithm. Experimental results show that the proposed algorithm has obvious advantages in scheduling quality in grid environment.

Keywords: grid computing; heterogeneous platform isomorphic task; memory; function task scheduling; distributed particle swarm optimisation; grid simulation.

DOI: 10.1504/IJWGS.2020.109473

International Journal of Web and Grid Services, 2020 Vol.16 No.3, pp.287 - 304

Received: 28 Dec 2019
Accepted: 22 Apr 2020
Published online: 09 Sep 2020 *

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