A novel power control framework in virtual resource environments

Recently, resource virtualisation technology has been widely applied in high-performance data centres. However, conventional power control techniques mainly concentrate on physical server instead of virtualised resource such virtual machine VM. In this paper, we present a novel power control framework called virtual resource power management and control vPMC, which applies control theory to achieve tradeoffs between power consumption and application performance. Extensive experiments are conducted to investigate the effectiveness and performance of the proposed framework. The results indicate that the vPMC framework can provide effective control on both power consumption and application performance. In addition, it can also significantly reduce the energy consumption of data centres through adaptive VM migration mechanism.

[1]  Hermann de Meer,et al.  Performance tradeoffs of energy-aware virtual machine consolidation , 2013, Cluster Computing.

[2]  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..

[3]  Shoubin Dong,et al.  An energy-aware heuristic framework for virtual machine consolidation in Cloud computing , 2014, The Journal of Supercomputing.

[4]  Ioannis Tomkos,et al.  Power consumption evaluation of all-optical data center networks , 2012, Cluster Computing.

[5]  Maziar Goudarzi,et al.  Structure-aware online virtual machine consolidation for datacenter energy improvement in cloud computing , 2015, Comput. Electr. Eng..

[6]  Stijn Eyerman,et al.  A Counter Architecture for Online DVFS Profitability Estimation , 2010, IEEE Transactions on Computers.

[7]  Chia-Ming Wu,et al.  A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters , 2014, Future Gener. Comput. Syst..

[8]  Karsten Schwan,et al.  VPM tokens: virtual machine-aware power budgeting in datacenters , 2009, Cluster Computing.

[9]  Zhiqiang Ma,et al.  DVM: A Big Virtual Machine for Cloud Computing , 2014, IEEE Transactions on Computers.

[10]  Kevin Skadron,et al.  Predictive Temperature-Aware DVFS , 2010, IEEE Transactions on Computers.

[11]  Abhishek Chandra,et al.  Exploiting Spatio-Temporal Tradeoffs for Energy-Aware MapReduce in the Cloud , 2012, IEEE Transactions on Computers.

[12]  Vanish Talwar,et al.  Loosely coupled coordinated management in virtualized data centers , 2010, Cluster Computing.

[13]  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.

[14]  Zhen Xiao,et al.  Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment , 2013, IEEE Transactions on Parallel and Distributed Systems.

[15]  Mateo Valero,et al.  Understanding the future of energy-performance trade-off via DVFS in HPC environments , 2012, J. Parallel Distributed Comput..

[16]  Layuan Li,et al.  Tradeoffs between energy consumption and QoS in mobile grid , 2011, The Journal of Supercomputing.

[17]  Xin Xu,et al.  DUAL: Reliability-Aware Power Management in Data Centers , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[18]  Longbo Huang,et al.  A Comment on “Power Cost Reduction in Distributed Data Centers: A Two Time Scale Approach for Delay Tolerant Workloads” , 2015, IEEE Transactions on Parallel and Distributed Systems.