计算机技术与发展2017，Vol.27Issue(3)：117-121,5.DOI:10.3969/j.issn.1673-629X.2017.03.024
基于蚁群模拟退火的云任务调度算法改进
Improvement of Algorithm for Cloud Task Scheduling Based on Ant Colony Optimization and Simulated Annealing
摘要
Abstract
With the rapid development of cloud computing,how to carry on task scheduling effectively is crucial in the research of cloud computing. Cloud task scheduling belongs to a NP-hard optimization problem,and many meta-heuristic algorithms have been proposed to solve it. ACO algorithm in task scheduling still has many shortcomings such as slow convergence speed,poor ability of local search and falling into local optimum easily. Therefore,a new algorithm-ACOSA is presented to solve task scheduling problem. In this algorithm,re-ducing task completion time and ensuring resource' s load balance as the goal,according to the local ant colony algorithm the optimal so-lution is constructed,and the strong local search capability of simulated annealing algorithm is applied to make the local optimal solutions as the initial solutions of simulated annealing algorithm and accept the results of current search to a certain probability in order to avoid falling into the local optimal. Simulation results show that ACOSA is superior to First Come First Served ( FCFS) and Ant Colony Opti-mization ( ACO) by reducing make span and achieving load balance.关键词
任务调度/云计算/蚁群算法/模拟退火算法Key words
task scheduling/cloud computing/ACO/Simulated Annealing分类
信息技术与安全科学引用本文复制引用
秦军,董倩倩,郝天曙..基于蚁群模拟退火的云任务调度算法改进[J].计算机技术与发展,2017,27(3):117-121,5.基金项目
江苏省自然科学基金项目(BK20130882) （BK20130882）
