A GA Based Multiple Task Allocation Considering Load

A. Tripathi, B. K. Sarker, Naveen Kumar, D. P. Vidyarthi
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引用次数: 33

Abstract

A Distributed Computing System (DCS) comprising networked heterogeneous processors requires ecient tasks to processor allocation to achieve minimum turnaround time and highest possible throughput. Task allocation in DCS remains an important and relevant problem attracting the attention of researchers in the discipline. A good number of task allocation algorithms have been proposed in the literature [3{9]. This algorithm considered allocation of the modules of a single task to various processing nodes and aim to minimize the turnaround time of the given task. But they did not consider execution of modules belonging to various dierent tasks (i.e. multiple tasks). In this work we have considered the number of modules that can be accepted by individual processing nodes along with their memory capacities and arrival of multiple disjoint tasks to the DCS from time to time. In this paper, a method based on genetic algorithm is developed which is memory ecient and give an optimal solution of the problem. The given simulation results also show signicant achievement in this regard.
基于遗传算法的考虑负载的多任务分配
由网络异构处理器组成的分布式计算系统(DCS)要求处理器分配高效的任务,以实现最短的周转时间和最高的吞吐量。DCS中的任务分配一直是该学科研究人员关注的一个重要而相关的问题。文献[3{9]中已经提出了大量的任务分配算法。该算法考虑将单个任务的模块分配到不同的处理节点,以最小化给定任务的周转时间为目标。但是他们没有考虑执行属于不同任务的模块(即多个任务)。在这项工作中,我们考虑了单个处理节点可以接受的模块数量以及它们的内存容量,以及多个不相交的任务不时到达DCS。本文提出了一种基于遗传算法的方法,该方法具有较好的内存利用率,并给出了该问题的最优解。给出的仿真结果也表明在这方面取得了显著的成就。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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