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Received:October 20, 2017 Revised:November 10, 2017
Received:October 20, 2017 Revised:November 10, 2017
中文摘要: 蚁群算法ACO能较好地应用于集群调度，但其传统的信息素更新方式带来了性能匹配和负载均衡等问题，影响了集群调度的性能.针对这些问题，提出了改进型蚁群算法IACO，通过引入性能匹配因子和负载均衡因子更合理地调整信息素，缩短了作业处理时间，提高了CPU利用率，从而有效地提高了集群调度性能.
Abstract:The Ant Colony Algorithm (ACO) can be applied to cluster scheduling better, but its traditional pheromone update method brings the performance matching and load balancing and so on, which affects the performance of cluster scheduling. In order to solve these problems, an Improved ACO (IACO) is put forward. The pheromone is adjusted more reasonably by adjusting the performance matching factor and the load balancing factor. It is observed that the processing time is shortened, the CPU utilization is improved, and the performance of the cluster is improved effectively.
keywords: cluster scheduling Ant Colony Algorithm (ACO) performance matching factor load balancing factor Improved ACO (IACO)
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基金项目:山东省自然科学基金（Z2016FM017）；中央高校基本科研业务费专项基金（16CX02046A）
引用文本：
刘素芹,张千,王俊爽.一种提高集群调度性能的改进型蚁群算法.计算机系统应用,2018,27(7):173-176
LIU Su-Qin,ZHANG Qian,WANG Jun-Shuang.Improved Ant Colony Algorithm for Improving Performance of Cluster Scheduling.COMPUTER SYSTEMS APPLICATIONS,2018,27(7):173-176
刘素芹,张千,王俊爽.一种提高集群调度性能的改进型蚁群算法.计算机系统应用,2018,27(7):173-176
LIU Su-Qin,ZHANG Qian,WANG Jun-Shuang.Improved Ant Colony Algorithm for Improving Performance of Cluster Scheduling.COMPUTER SYSTEMS APPLICATIONS,2018,27(7):173-176
