Virtual Machine Based Energy-Efficient Data Center Architecture for Cloud Computing: A Performance Perspective

Virtual machine technology is widely applied to modern data center for cloud computing as a key technology to realize energy-efficient operation of servers. Server consolidation achieves energy efficiency by enabling multiple instantiations of operating systems (OSes) to run simultaneously on a single physical machine. While, live migration of virtual machine can transfer the virtual machine workload from one physical machine to another without interrupting service. However, both the two technologies have their own performance overheads. There is a tradeoff between the performance and energy efficiency. In this paper, we study the energy efficiency from the performance perspective. Firstly, we present a virtual machine based energy-efficient data center architecture for cloud computing. Then we investigate the potential performance overheads caused by server consolidation and live migration of virtual machine technology. Experimental results show that both the two technologies can effectively implement energy-saving goals with little performance overheads. Efficient consolidation and migration strategies can improve the energy efficiency.

[1]  Yingwei Luo,et al.  Live and incremental whole-system migration of virtual machines using block-bitmap , 2008, 2008 IEEE International Conference on Cluster Computing.

[2]  Margaret Martonosi,et al.  Dynamic thermal management for high-performance microprocessors , 2001, Proceedings HPCA Seventh International Symposium on High-Performance Computer Architecture.

[3]  Karsten Schwan,et al.  VirtualPower: coordinated power management in virtualized enterprise systems , 2007, SOSP.

[4]  Kartik Gopalan,et al.  Post-copy based live virtual machine migration using adaptive pre-paging and dynamic self-ballooning , 2009, VEE '09.

[5]  James J. Kistler,et al.  Challenges, Techniques and Directions in Building XSeek: an XML Search Engine. , 2009 .

[6]  Vanish Talwar,et al.  No "power" struggles: coordinated multi-level power management for the data center , 2008, ASPLOS.

[7]  Eugene Ciurana,et al.  Google App Engine , 2009 .

[8]  Hai Jin,et al.  Energy optimization schemes in cluster with virtual machines , 2010, Cluster Computing.

[9]  Richard Wolski,et al.  The Eucalyptus Open-Source Cloud-Computing System , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[10]  A. Kivity,et al.  kvm : the Linux Virtual Machine Monitor , 2007 .

[11]  Ricardo Bianchini,et al.  Conserving disk energy in network servers , 2003, ICS '03.

[12]  Xiaohong Jiang,et al.  vTestkit: A Performance Benchmarking Framework for Virtualization Environments , 2010, 2010 Fifth Annual ChinaGrid Conference.

[13]  Liang Liu,et al.  GreenCloud: a new architecture for green data center , 2009, ICAC-INDST '09.

[14]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[15]  Xiaomin Zhang,et al.  Characterization & analysis of a server consolidation benchmark , 2008, VEE '08.

[16]  Carl A. Waldspurger,et al.  Memory resource management in VMware ESX server , 2002, OSDI '02.

[17]  Erol Gelenbe,et al.  Energy-Efficient Cloud Computing , 2010, Comput. J..

[18]  Sergiu Nedevschi,et al.  Reducing Network Energy Consumption via Sleeping and Rate-Adaptation , 2008, NSDI.

[19]  RanganathanParthasarathy,et al.  No "power" struggles , 2008 .

[20]  Andrew Warfield,et al.  Live migration of virtual machines , 2005, NSDI.

[21]  Hai Jin,et al.  Live migration of virtual machine based on full system trace and replay , 2009, HPDC '09.

[22]  Hai Jin,et al.  Live virtual machine migration with adaptive, memory compression , 2009, 2009 IEEE International Conference on Cluster Computing and Workshops.

[23]  James J. Kistler,et al.  Building a Cloud for Yahoo! , 2009, IEEE Data Eng. Bull..