云计算环境下基于ABC-QPSO算法的资源调度模型  

ABC AND QPSO ALGORITHM-BASED RESOURCE SCHEDULING MODEL IN CLOUD COMPUTING ENVIRONMENT

作　　者：温聪源[1] 徐守萍[1] 曾致远[1] 

机构地区：[1]广东金融学院,广东广州510520

出　　处：《计算机应用与软件》2015年第5期30-32,64,共4页Computer Applications and Software

基　　金：广东省教育科学规划课题(2012JK063)

摘　　要：为了提高云计算资源的利用率,保证节点负载均衡,提出一种人工蜂群算法和量子粒子群算法相融合的云计算资源调度模型(ABC-QPSO)。首先将人工蜂群算法的搜索算子作为变异算子引入到量子粒子群算法中,以解决量子粒子群算法早熟收敛缺陷,然后以任务完成时间最短作为量子粒子群的适应度函数对云计算资源调度进行优化,最后在Cloud Sim平台上对ABC-QPSO的性能进行测试。结果表明,ABC-QPSO算法不仅克服了QPSO算法的不足,而且有效缩短了任务的完成时间,提高了云计算资源利用率,适合于进行大规模任务的云计算资源调度。In order to improve the utilisation rate of cloud resource scheduling and guarantee load balance of nodes, we propose a cloud computing resource scheduling method which combines the quantum particle swarm optimisation algorithm with artificial bee colony algorithm. Firstly, the search operator of artificial bee colony algorithm is introduced to QPSO algorithm as the mutation operator to solve its defect of premature convergence, and then the shortest scheduling time is taken as the fitness function of the QPSO algorithm to optimise the cloud resource scheduling, finally, the performance test of ABC-QPSO is carried out on CloudSim platform. Results show that the ABC-QPSO overcomes the shortcomings of QPSO algorithm, and effectively shortens the task completion time as well, as well as improves the utilisation rate of cloud resource scheduling, it is suitable for cloud computing resource scheduling of large-scale tasks.

关 键 词：云计算 资源调度 人工蜂群算法 量子粒子群算法 任务分配 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]