基于模糊聚类分析的云计算负载平衡策略

如何实现资源访问的负载平衡成为云计算实施的关键问题之一。基于云计算环境的特点，改进了模糊聚类算法，将粒子群优化算法与模糊C均值聚类算法融合，提高算法正确率。将改进后的聚类算法应用于对各个计算节点的输入输出(I/O)及中央处理器(CPU)利用率的分析,得到对于负载度的分类，并以此为依据判断需要迁移任务的节点，进而实现负载平衡。实验结果表明无论是在UCI机器学习库或是针对提出的负载平衡机制环境下,改进的模糊聚类算法在算法的准确率方面均优于传统算法10%以上，且在算法稳定性方面亦优于传统算法。

Load balance strategy of cloud computing based on fuzzy clustering analysis

It plays an important role in realizing cloud computing to implement the load balance of accessing resources. Therefore, based on the characteristics of cloud computing environment, an improved fuzzy clustering analysis algorithm was proposed in this paper. Furthermore, integrating Particle Swarm Optimization (PSO) algorithm with fuzzy C-means algorithm improved the algorithm accuracy. Then, using the improved fuzzy clustering algorithm in analyzing utilization ratio of I/O and Central Processing Unit (CPU) of all computing nodes, each node was divided into an affirmatory collection. And it represented its load level, as a basis for judging the node which needed to transfer tasks, so as to achieve the load balance. According to the experimental results, in terms of algorithm accuracy, no matter what is in the UCI machine learning repository, or for the proposed load balancing mechanism, the improved fuzzy clustering algorithm's reached the traditional one's by 110%. Besides, it surpasses the traditional one in the stability of the algorithm.