云环境下基于DPSO的任务调度算法  

Task Scheduling Algorithm Based on DPSO Under Cloud Environment

作　　者：邬开俊[1] 鲁怀伟[2] 

机构地区：[1]兰州交通大学电子与信息工程学院,兰州730070 [2]兰州交通大学数理与软件工程学院,兰州730070

出　　处：《计算机工程》2014年第1期59-62,共4页Computer Engineering

基　　金：国家社科基金资助项目"突发事件应急物资调度模型及优化算法研究"(12CGL004);甘肃省科技支撑计划基金资助项目(1304FKCA097);甘肃省高等学校科研基金资助项目(2013A-052);兰州交通大学青年科学研究基金资助项目(2011005)

摘　　要：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。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.

关 键 词：云计算 粒子群优化 离散 任务调度 惯性权重 

分 类 号：TP306.1[自动化与计算机技术—计算机系统结构]