一种特征加权模糊聚类的负载均衡算法  

Fuzzy clustering based load balancing algorithm with feature weighted

作　　者：黄伟华[1] 马中[1] 戴新发[1] 徐明迪[1] 高毅[1] 刘利民[1] HUANG Weihua MA Zhong DAI Xinfa XU Mingdi GAO Yi LIU Limin(Wuhan Digital Engineering Institute, Wuhan 430074, Chin)

机构地区：[1]武汉数字工程研究所,湖北武汉430074

出　　处：《西安电子科技大学学报》2017年第2期127-132,共6页Journal of Xidian University

基　　金：国家自然科学基金资助项目(61502438)

摘　　要：针对负载均衡算法中多类负载的融合问题,提出了一种基于特征加权模糊聚类的负载均衡算法.首先,将不同系统资源作为负载度量的一个维度,并针对不同维度进行特征加权,实现了对综合负载的量化;然后,引入模糊聚类方法,优化了权重约束,并增加惩罚项,以此对负载进行聚类划分,为负载迁移定位最优目标节点簇.实验结果表明,该算法能够融合多维负载数据,与经典算法相比,集群中节点负载的标准差减小了21%.Focusing on the data fusion problem of various loads, a fuzzy clustering based load balancing algorithm with feature weighted is proposed. First of all, various system resources are considered as dimensions for load metrics, and features for different dimensions are weighted so as to quantify comprehensive loads; then, this algorithm introduces fuzzy clustering, optimizes weight constraints, and adds penalty terms. Hence, the most suitable objective node cluster for load transferring is resolved through fuzzy clustering. Experimental results show that this algorithm can effectively fuse multidimensional load data and reduce standard deviation for node loads within the cluster by 21% compared with existing algorithms.

关 键 词：负载均衡 模糊聚类 特征加权 目标函数 熵 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]