基于离散人工蜂群算法的云任务调度优化

针对现今云计算任务调度只考虑单目标和云计算应用对虚拟资源的服务的质量要求高等问题,综合考虑了用户最短等待时间、资源负载均衡和经济原则,提出一种离散人工蜂群(ABC)算法的云任务调度优化策略。首先,从理论上建立了云任务调度的多目标数学模型;然后,结合偏好满意度策略并引入局部搜索算子和改变侦察蜂搜索方式,提出多目标离散型人工蜂群(MDABC)算法的优化策略。通过不同的云任务调度仿真实验,显示了改进离散人工蜂群算法相对于基础离散人工蜂群算法、遗传算法以及经典贪心算法,能够得到较高的综合满意度,表明了改进离散人工蜂群算法能够更好地改善虚拟资源中云任务调度系统的性能,具有一定的普适性。

Optimization of cloud task scheduling based on discrete artificial bee colony algorithm

To meet high quality requirement of virtual resource service in cloud computing applications and solve the problem that cloud computing task scheduling only consider single objective currently, a Discrete Artificial Bee Colony (DABC) algorithm for cloud task scheduling optimization was proposed by considering the users' shortest waiting time, resource load balancing and economic principle. First, the multi-objective mathematical model of cloud task scheduling was established in theory. Second, by combining with preference satisfaction policy, introducing the local search operator and changing the searching way of scout bee, an optimizing strategy based on the Multi-objective DABC (MDABC) algorithm was proposed to solve the problem. Different cloud task scheduling simulation experimental results show that the proposed MDABC algorithm can obtain higher comprehensive satisfaction than the basic DABC algorithm, Genetic Algorithm (GA) and classical greedy algorithm. Thus, the proposed MDABC algorithm can better improve the performance of cloud task scheduling in virtual resource system, and its universality is better.