一种提高集群调度性能的改进型蚁群算法  

Improved Ant Colony Algorithm for Improving Performance of Cluster Scheduling

作　　者：刘素芹[1] 张千[1] 王俊爽 LIU Su-Qin;ZHANG Qian;WANG Jun-Shuang(College of Computer ＆Communication Technology, China University of Petroleum, Qingdao 266580, Chin)

机构地区：[1]中国石油大学(华东)计算机与通信工程学院,青岛266580

出　　处：《计算机系统应用》2018年第7期173-176,共4页Computer Systems & Applications

基　　金：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)~~

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.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.

关 键 词：集群调度 ACO 性能匹配因子 负载均衡因子 IACO 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]