基于负载均衡蚁群优化算法的云计算任务调度

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基金项目:

国家自然科学基金资助项目(61072043)

Task Scheduling Algorithm Based on Load Balancing Ant Colony Optimization in Cloud Computing

摘要: 合理分配虚拟计算资源以有效进行任务调度是云计算中的一项核心问题.针对云计算任务调度过程中资源负载不均的问题,根据虚拟机负载情况提出信息素调整因子(pheromone adjustment factor,PAF),改进信息素更新规则.提出基于负载平衡的蚁群优化算法(Load Balancing Ant Colony Optimization,LBACO).改进的调度策略在云仿真平台CloudSim上进行实验,结果表明LBACO算法不仅能降低任务执行时间,还可有效保持云数据中心虚拟机资源负载平衡.
- 云计算 /
- 任务调度 /
- 蚁群算法 /
- 负载均衡
Abstract: Reasonable virtual machine allocating and efficient task scheduling is a key problem in the cloud computing. A pheromone adjustment factor is given out according to the load of virtual machines, and a LBACO(Load Balancing Ant Colony Optimization) algorithm is proposed to solve the load imbalance of virtual machine in the process of task scheduling. The LBACO algorithm can adapt to the dynamic cloud environment. The new scheduling strategy was simulated on the CloudSim platform. The results show that the proposed LBACO algorithm not only shorten the makespan of task scheduling, but also maintain the load balance of virtual machines in the data center.
- cloud computing /
- task scheduling /
- ACO(ant colony optimization) /
- load balancing