云计算环境下基于蚁群优化的任务负载均衡调度算法

摘要

随着云计算的蓬勃发展，针对云计算中虚拟机负载不均衡及任务集完成时间较长的问题，提出了一种基于蚁群优化的任务负载均衡调度算法(WLB-ACO)。首先基于当前虚拟机的资源利用情况判断虚拟机的负载状态，其次，根据虚拟机的负载因子定义信息素的挥发因子(w)，改进信息素更新规则，并利用WLB-ACO合理的分配任务，使整个系统达到负载均衡状态的同时任务集的完成时间最短。最后，采用Cloudsim工具设计仿真实验，实验结果表明，提出的基于蚁群优化的任务调度算法在性能、最短完成时间及算法的稳定收敛性上有了一定的提高。%With the rapid development of cloud computing, cloud computing for the virtual machine load imbalance and take a long time to complete tasks set , it is proposed based on ant colony optimization task scheduling algorithm load balancing (WLB-ACO). Firstly, the current virtual machine based on the resource utilization in order to determine a load state of the virtual machine , and secondly, the pheromone volatile factors (w) is defined by virtual machine 's load factor. To improve the pheromone update rules , and use WLB-ACO rational allocation of tasks , completion of the entire system to achieve load balancing state while the minimum set of tasks . Finally , the use of simulation tools to design Cloudsim experimental results show that the task scheduling based on ant colony optimization algorithm in terms of performance, the shortest time to complete a certain increase stability and convergence of the algorithm.