Adaptive Multi-Threshold Energy-Aware Virtual Machine Consolidation in Cloud Data Center

The ever-increasing energy consumption in cloud data centers not only translates to high operating costs, but also leads to negative impact on environment. Dynamic consolidation of virtual machine (VM) is proven to be an efficient way to improve resource utilization and reduce energy consumption in cloud data centers. In this paper, both the CPU utilization of system and SLA of users are taken into account to classify hosts and an adaptive multi-threshold energy-aware virtual machine consolidation algorithm is proposed to provide different consolidation mechanisms for different types of hosts. First, compound threshold is designed for overload hosts and will be adjusted dynamically to ensure both CPU utilization and SLA. Then a Q-Iearning based method is proposed to further divide underload hosts to save energy. Experiment results show that, our proposed algorithm can optimize resource utilization and reduce energy consumption of the data centers while minimizing the SLA violation rate and the number of migrations.

[1]  Maher Khemakhem,et al.  Energy management strategy in cloud computing: a perspective study , 2017, The Journal of Supercomputing.

[2]  Nagarajan Kandasamy,et al.  Power and performance management of virtualized computing environments via lookahead control , 2008, 2008 International Conference on Autonomic Computing.

[3]  Jie Wu,et al.  A Multi-objective Biogeography-Based Optimization for Virtual Machine Placement , 2015, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[4]  Mohamed Elhoseny,et al.  Energy consumption analysis of Virtual Machine migration in cloud using hybrid swarm optimization (ABC–BA) , 2018, The Journal of Supercomputing.

[5]  Jemal H. Abawajy,et al.  Energy-efficient virtual machine consolidation algorithm in cloud data centers , 2017 .

[6]  Pasi Liljeberg,et al.  Self-Adaptive Resource Management System in IaaS Clouds , 2016, 2016 IEEE 9th International Conference on Cloud Computing (CLOUD).

[7]  Enda Barrett,et al.  Applying reinforcement learning towards automating resource allocation and application scalability in the cloud , 2013, Concurr. Comput. Pract. Exp..

[8]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[9]  Shahin Vakilinia.  Energy efficient temporal load aware resource allocation in cloud computing datacenters , 2017, Journal of Cloud Computing.

[10]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[11]  Mohamadreza Ahmadi,et al.  A dynamic VM consolidation technique for QoS and energy consumption in cloud environment , 2017, The Journal of Supercomputing.

[12]  Francesco De Pellegrini,et al.  A Framework for Allocating Server Time to Spot and On-Demand Services in Cloud Computing , 2019, ACM Trans. Model. Perform. Evaluation Comput. Syst..

[13]  Amol C. Adamuthe,et al.  Multiobjective Virtual Machine Placement in Cloud Environment , 2013, 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies.

[14]  Mohamed Othman,et al.  Energy-Efficient Algorithms for Dynamic Virtual Machine Consolidation in Cloud Data Centers , 2017, IEEE Access.

[15]  Luiz André Barroso,et al.  The Case for Energy-Proportional Computing , 2007, Computer.

[16]  KyoungSoo Park,et al.  CoMon: a mostly-scalable monitoring system for PlanetLab , 2006, OPSR.

[17]  Pasi Liljeberg,et al.  Energy-Efficient Virtual Machines Consolidation in Cloud Data Centers Using Reinforcement Learning , 2014, 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.

[18]  Xiaoyun Zhu,et al.  1000 Islands: Integrated Capacity and Workload Management for the Next Generation Data Center , 2008, 2008 International Conference on Autonomic Computing.

[19]  Rajkumar Buyya,et al.  Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers , 2012, Concurr. Comput. Pract. Exp..

[20]  Li Dan,et al.  Leveraging Renewable Energy in Cloud Computing Datacenters: State of the Art and Future Research , 2014 .

[21]  Rajkumar Buyya,et al.  Energy Efficient Allocation of Virtual Machines in Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[22]  Abolfazl Toroghi Haghighat,et al.  Energy-aware framework with Markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers , 2017, The Journal of Supercomputing.