Volume 29 Issue 6
Dec.  2020
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LI Lianpeng, DONG Jian, ZUO Decheng, JI Songyan. SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State 3rd-Order Markov Chain Model[J]. Chinese Journal of Electronics, 2020, 29(6): 1207-1217. doi: 10.1049/cje.2020.10.008
Citation: LI Lianpeng, DONG Jian, ZUO Decheng, JI Songyan. SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State 3rd-Order Markov Chain Model[J]. Chinese Journal of Electronics, 2020, 29(6): 1207-1217. doi: 10.1049/cje.2020.10.008

SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State 3rd-Order Markov Chain Model

doi: 10.1049/cje.2020.10.008
Funds:  This work is supported by the National Natural Science Foundation of China (No.61100029).
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  • Corresponding author: DONG Jian (corresponding author) received the Ph.D. degree in computer science and technology from HIT in 2008. From 2012 to 2017, he was an associate professor with the School of Computer Science and Technology, HIT. Since 2017, he has been a professor at the same school. His research interests include fault-tolerant computing, parallel computation, and cloud computing. (Email:dan@hit.edu.cn)
  • Received Date: 2019-10-21
  • Publish Date: 2020-12-25
  • Virtual machine (VM) consolidation offers a promising approach to saving energy and improving resource utilization in cloud data centers. However, the aggressive consolidation of VMs may lead to Servicelevel agreement (SLA) violations, which are essential for data centers and their users. Therefore, it is essential to find a tradeoff between the reduction in SLA violation level and energy costs. In this paper, we improve and expand our Host state 3rd-order Markov chain (HS3MC) model proposed in our previous research comprehensively: we propose a new VM selection algorithm, an improved VM placement algorithm, and an HS3MC-based VM consolidation algorithm based on our improved HS3MC model for the SLA-aware and energy-efficient consolidation of VMs in cloud data centers. We evaluate our proposed algorithms on an extended Cloudsim simulator using the PlanetLab workload and a random workload. Specifically, we improve and expand the OpenStack Neat experimental platform, and evaluate the performance of our algorithms in a real cloud environment based on OpenStack. The experimental results show that our proposed model can significantly reduce the SLA violation rates while maintaining energy efficiency.
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