An Energy-Aware and Under-SLA-Constraints VM Consolidation Strategy Based on the Optimal Matching Method

WeiLing Li, Yongbo Wang, Yuandou Wang, YunNi Xia, Xin Luo and Quanwang Wu
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WeiLing Li: School of Software Theory and Technology Chongqing Key Lab, Chongqing University, Chongqing, China
Yongbo Wang: School of Software Theory and Technology Chongqing Key Lab, Chongqing University, Chongqing, China
Yuandou Wang: School of Software Theory and Technology Chongqing Key Lab, Chongqing University, Chongqing, China
YunNi Xia: School of Software Theory and Technology Chongqing Key Lab, Chongqing University, Chongqing, China
Xin Luo: Chinese Academy of Sciences, Chongqing Institute of Green and Intelligent Technology, Chongqing, China
Quanwang Wu: School of Software Theory and Technology Chongqing Key Lab, Chongqing University, Chongqing, China

International Journal of Web Services Research (IJWSR), 2017, vol. 14, issue 4, 75-89

Abstract: Growing demand of computational power brings increasing scale and complexity of cloud datacenters. However, such increase also generates growing energy consumption and related cost incurred for cooling and maintenance. With concerns of cost and energy saving by both industry and academy, the reduction of energy consumption of cloud datacenters becomes a hotspot issue. Recently, virtual-machine-consolidation-based strategies are proposed as promising methods for reduction of cloud energy consumption. Virtual machine (VM) consolidation effectively increases the resource utilization rate. However, it remains a great challenge how to reduce energy consumption while maintaining the quality of service (QoS) at a satisfactory level. In this work, a comprehensive framework is presented for the above-mentioned problem, which aims at maximizing the number of physical machines (PMs) to be turned off within a consolidation period following the constraints of QoS, in terms of Service-Level-Agreement (SLA) violation rate. In comparison with most existing related works which consider invariant utilization rate of PMs in computing energy reduction of candidate migration plans, propose framework considers time-varying utilization rate and employs the number of PMs to be turned off within a consolidation period (NPTCP for simple) as the optimization objective. The proposed framework consists of a resource selection algorithm taking the predicted migration overhead (derived by the Pareto distribution) as inputs and another algorithm generating optimal matching plans based on preference scores of candidate VMs. For the model validation purpose, a case study is conducted on the CloudSim simulation platform and it shows that the proposed method achieves better energy reduction and less SLA violation.

Date: 2017
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