基于云计算服务器的二次粒子群任务调度算法

Guanghui Wei
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引用次数: 1

摘要

任务调度是云计算的核心问题之一,其目的是合理分配任务,实现最优调度策略,提高整个云计算系统的运行效率。针对传统粒子群优化算法在总完工时间和平均完工时间上的不足,提出了一种二次粒子群优化算法。利用所提出的算法,可以在保证任务总完成时间短的情况下,找到一个任务平均完成时间短的调度结果。最后,利用Cloud Sim软件进行了仿真实验。实验结果表明,在相同的条件设置下,该算法优于传统的粒子群算法。当任务数量增加时,QPSO的综合调度性能比PSO提高20%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quadratic Particle Swarm Optimisation Algorithm for Task Scheduling Based on Cloud Computing Server
The task scheduling is one of the core problems of cloud computing and aims to assign tasks reasonably, realise the optimal scheduling strategy and improve the operating efficiency of overall cloud computing system. For the shortcomings of traditional particle swarm optimisation (PSO) algorithm in total completion time and average completion time, a quadratic particle swarm optimisation (QPSO) algorithm is proposed. Using the proposed algorithm, people can find a scheduling result with the short total completion time of task and also ensuring the short average completion time of task. Finally, the research made a simulation experiment with Cloud Sim. Experiment results show that in the same condition setting, the algorithm proposed is superior to the traditional PSO algorithm. When the number of tasks increases, the comprehensive scheduling performance of QPSO is more than 20% higher than that of PSO.
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