面向云数据中心资源均衡分配需求的聚类调度算法研究

针对云数据中心资源利用率较低、能源消耗较高的问题,提出了基于资源需求差异的资源均衡调度策略。在包簇框架模型基础上,利用与资源需求相关的距离度量因子,将资源需求差异大的包通过改进的k-means算法进行聚类;利用资源之间的相关性作为包与簇之间的距离,在资源分配的过程中使包能够集中映射到簇中,从而减少簇的使用个数。实验结果表明,在包簇框架的概念下,基于资源需求差异的改进后的k-means聚类算法能够优化包聚类步骤,资源调度算法能够提高云数据中心各类资源利用率、降低资源分配过程中产生的能耗,具有有效性和可扩展性。

Clustering scheduling algorithm for resource allocation requirements of cloud data centers

Aiming at the problem of low resource utilization and high energy consumption in cloud data center, a resource balancing scheduling strategy based on the resource demand difference was proposed. Based on a package-cluster framework model, the packages with large differences in resource requirements were clustered an improved k-means algorithm using the distance metrics related to resource requirements. The resources were used as the distance between packages and clusters. In the process of resource allocation, the package was mapped into clusters in a centralized manner, thereby the number of clusters used could be reduced. The experimental results show that under the concept of package-cluster framework, the improved k-means clustering algorithm based on the difference of resource requirements can optimize the packet clustering step. The resource scheduling algorithm presented can improve the utilization of various resources and reduce the energy consumption in the cloud data center. The algorithm is of effectiveness and scalability.