文献类型：期刊文献

英文题名：Performance-to-Power Ratio Aware Resource Consolidation Framework Based on Reinforcement Learning in Cloud Data Centers

作者：Ding, Weichao[1];Luo, Fei[1];Gu, Chunhua[1];Lu, Haifeng[1];Zhou, Qin[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：8

起止页码：15472

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20200908221169);WOS:【SCI-EXPANDED(收录号:WOS:000524741300016)】；

基金：This work was supported in part by the Project on Educational Teaching Law and Method of East China University of Technology, in part by the Online Education Fund (Pervasive Education) of Online Education Research Center in Chinese Ministry of Education under Grant 2017YB122, and in part by the National Natural Science Foundation of China (NSFC) under Grant 61472139.

语种：英文

外文关键词：Resource consolidation; reinforcement learning; energy consumption; SLA violation

摘要：Dynamic consolidation of virtual machines (VMs) is presented as a significant technique of energy conservation in cloud environments, which can eliminate the hotspot of overloaded hosts and switch the under loaded hosts to sleep mode through live migration of virtual machines. However, since the fact that migrating VM consumes a certain amount of extra resources, the process of reallocation can cause Service Level Agreement (SLA) violations. In this paper, a novel proactive framework which considers both predicted resource utilization and Performance-to-power Ratio (PPR) of heterogeneous hosts is proposed to perform dynamic VM consolidation to achieve balance of performance and energy. More precisely, a workload predictor is proposed based on the modified Weighted Moving Average (WMA) algorithm, representing the support for dynamic resource allocation; a cluster controller is proposed based on reinforcement learning for exploring the optimal matching relationship between resource requests and host at different PPR levels; a resource allocator is designed based on greedy strategy for achieving the trade-off between energy consumption and application performance across the cluster. Moreover, the framework is implemented based on distributed architecture and off-line learning pattern, which are able to not only scale up quickly but also improve the computing efficiency of the system. To validate the effectiveness of the proposed method, we have performed experimental evaluation on CloudSim with real-world workload traces of PlanetLab, and the simulation results demonstrate that it reduces the energy consumption up to 45.25 and effectively deals with high Quality of Service (QoS) requirements and heterogeneous distributed infrastructures in comparison with other competitive approaches.