基于能耗感知的包簇资源分配算法研究

Energy-aware Sensing Optimization Algorithm for Cloud Computing Resources Based on Packet Clustering Framework

随着数据中心的扩大,现有的云计算资源的分配与调度机制,带来了许多庞大的工作调度问题,针对该问题,本文采用基于包簇框架的资源调度分配方式,并在此框架上,通过提出非线性能耗模型,在最大限度地不违背服务级别协议(SLAS)的情况下,并且同时以降低能耗为目标,提出基于BFDP(Best-Fit-Decreasing-Power)算法进行包簇资源的分配。通过实验表明,基于本文提出的包簇框架的能耗感知算法比较其他的适应算法,簇的使用个数降低,能耗根据系统各组件的利用率增高而降低。

With the expansion of the data center, the existing cloud computing resource allocation and scheduling mechanism brings a huge redundant work problem. For this problem, this paper adopts the resource scheduling method of the clustering framework, and proposes energy consumption in this framework. The perceptual model proposes a BFDP (Best-Fit-Decreasing-Power) algorithm to allocate cluster resources without violating the Service Level Agreement (SLAS), and at the same time can reduce energy consumption. Experiments show that compared with other adaptive algorithms based on the energy-aware algorithm of the clustering framework, the number of clusters used is reduced, and the energy consumption is reduced according to the utilization of all aspects of the system.