基于云计算的ACO-K中心点资源优化算法  

ACO-K medoids resource optimization algorithm based on cloud computing

作　　者：孟颖[1] 罗可[1] 刘建华[1] 姚丽娟[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,长沙410114

出　　处：《计算机工程与应用》2013年第5期103-107,219,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.11171095;No.10871031);湖南省自然科学衡阳联合基金(No.10JJ8008);湖南省科技计划项目(No.2011FJ3051);湖南省教育厅重点项目(No.10A015)

摘　　要：云计算是计算网络模型研究的热点领域,能实现几种资源共享和资源动态配置。然而,云计算中存储资源如何快速路由,减少动态负荷,兼顾全局负载平衡是有待解决的问题。ACO是一种仿生优化算法,具有健壮性强、智能搜索、全局优化、易与其他算法结合等优点。K中心点算法是K均值的改进算法,鲁棒性强,不易受极端数据的影响。结合这两种算法的优点,提出一种基于云计算环境下的ACO-K中心点资源分配优化算法,得到最优的计算资源,提高云计算的效率。通过仿真验证了该算法的有效性。Cloud computing has received increasingly attention from network computing model research, which can realize several kinds of resource sharing and dynamic resource allocation. However, how to effectively route storage resource in cloud, reduce dynamic load and take into account global load balancing are important problems to be solved. ACO is a bionics optimization algorithm with advantages of strong robustness, intelligent search, global optimization, easy to combine with other algorithms. K-medoids is an improved algorithm of k-means, of strong robustness and less susceptible to the impact of extreme data. Com- bined with priorities of these two algorithms, this paper proposes a kind of ACO-K-medoids resource allocation and optimization algorithm based on cloud computing. The algorithm can get the optimal computing resources and improve efficiency of cloud computing. Simulation experiments in the end of paper verify the efficiency of this algorithm.

关 键 词：云计算 资源分配 K中心点算法 蚁群算法(ACO) 动态负荷 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]