一种云计算环境下的组合寻优调度算法*

分布式计算环境中可将大作业进行任务分解,对分解后的一系列短作业采用最优化调度策略以达到缩短大作业整体周转时间和系统响应时间目的。针对传统调度策略的不足及云计算中网络延迟较大的特点,拟在云计算环境虚拟层对资源进行重新分配,根据自定义当前虚拟主机(KVM)的执行能力对其进行动态排序,采用改进的M_V_O蚁群算法对带有偏序关系的一系列短作业进行组合寻优调度,考虑到了云计算软件定义网络中的延时等因素局部更新蚂蚁的信息素浓度,并通过全局正向反馈增强最优解的收敛速度。本文理论上分析了该算法的有效性,且在CloudSIM平台下通过实验验证了该算法的可行性和有效性。

A Combined Optimization Scheduling Algorithm for Cloud Computing Environment

A long job can be split into a series of short ones on distributed computing environment. And taking optimal scheduling strategy to the divided short jobs, the algorithm try to get the target of shortening the turnaround time and system response time of the long job. Being directed against the shortcomings of traditional scheduling policies and the characteristics of network delay in cloud computing, the paper aims to reallocate the resource in the virtual level with the KVM web hosts. And this paper dynamically sorted the KVM web hosts by the customized operation capability, and took modified M_V_O ant colony algorithms toSdo taskSscheduling combinatorial optimization for the short jobs with partial orders, and locally updated the thickness of pheromones considered the delaying factors in the network, and accelerated optimization convergence speed by global positive feedback. The paper analyzed the validity of the algorithmsStheoretically, and verified the performance and feasibility of the algorithms under the platform of CloudSIM.