云环境下基于DPSO的任务调度算法

针对云计算任务调度问题,结合粒子群优化(PSO)算法的种群个体协作和信息共享特点,提出一种基于离散粒子群优化(DPSO)的任务调度算法。采用随机方法生成初始种群,利用时变方式调整惯性权重,并在位置更新中使用绝对值取整求余映射法进行合法化处理,提高PSO算法的离散化程度。搭建并重新编译了CloudSim云计算仿真平台进行实验,结果显示,当迭代次数为200时,DPSO、PSO、GA算法的所有任务最终调度时间分别为457.69 s、467.90 s、472.41 s,从而证明DPSO算法能够有效解决云计算环境下的任务调度问题,并且算法收敛速度优于PSO和GA算法。

Task Scheduling Algorithm Based on DPSO Under Cloud Environment

Aiming at the problem of cloud computing task scheduling, this paper combines the characteristics of population individual cooperation and information sharing of Particle Swarm Optimization（PSO）, and proposes a task scheduling algorithm based on Discrete Particle Swarm Optimization（DPSO）. In the algorithm, randomization method is used to generate the initial population, time-varying mode is used to adjust the inertia weight. During the location updating, the mapping of the rounded remainder of absolute value method is legalized to improve the discretization of PSO. The cloud computing simulation platform CloudSim is built and recompiled, the experimental results of iterations of 200 times show that DPSO, PSO and GA algorithm are respectively optimized to 457.69 s, 467.90 s and 472.41 s, so to prove that the DPSO algorithm can effectively solve the problem of task scheduling under cloud environment, and the alporithm is better than PSO and GA algorithm in convergence speed.