›› 2014, Vol. 29 ›› Issue (4): 631-645.doi: 10.1007/s11390-014-1455-4

Special Issue: Computer Architecture and Systems

• Computer Architectures and Systems • Previous Articles     Next Articles

Autonomic Performance and Power Control on Virtualized Servers:Survey, Practices, and Trends

Xiaobo Zhou1,2 (周笑波), Senior Member, IEEE, Member, ACM, and Chang-Jun Jiang2,3 (蒋昌俊), Member, CCF, IEEE   

  1. 1. Department of Computer Science, University of Colorado, Colorado Springs, U.S.A.;
    2. The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University Shanghai 200092, China;
    3. Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
  • Online:2014-07-05 Published:2014-07-05
  • About author:Xiaobo Zhou is a professor and the chair of computer science, University of Colorado, Colorado Springs. He received the B.S., M.S., and Ph.D. degrees in computer science from Nanjing University, in 1994, 1997, and 2000, respectively. He was a post-doctoral researcher at the University of Paderborn in 2000. His research lies broadly in computer network systems, more specifically, autonomic and sustainable computing in datacenters, Cloud computing, server virtualization, scalable Internet services and architectures.
  • Supported by:

    This research was supported in part by the National Science Foundation of USA under Grant Nos. CNS-0844983 (CAREER Award) and CNS-1217979, and the National Natural Science Foundation of China under Grant No. 61328203.

Modern datacenter servers hosting popular Internet services face significant and multi-facet challenges in performance and power control. The user perceived performance is the result of a complex interaction of complex workloads in a very complex underlying system. Highly dynamic and bursty workloads of Internet services fluctuate over multiple time scales, which have a significant impact on processing and power demands of datacenter servers. High density servers apply virtualization technology for capacity planning and system manageability. Such virtualized computer systems are increasingly large and complex. This paper surveys representative approaches to autonomic performance and power control on virtualized servers, which control the quality of service provided by virtualized resources, improve the energy efficiency of the underlying system, and reduce the burden of complex system management from human operators. It then presents three designed self-adaptive resource management techniques based on machine learning and control for percentile-based response time assurance, non-intrusive energy-efficient performance isolation, and joint performance and power guarantee on virtualized servers. The techniques were implemented and evaluated in a testbed of virtualized servers hosting benchmark applications. Finally, two research trends are identified and discussed for sustainable cloud computing in green datacenters.

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