Energy Consumption Measurement and Management in Cloud Computing Environment
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National Natural Science Foundation of China (61402183); Natural Science Foundation of Guangdong Province of China (S2012030006242); Guangdong Provincial Science and Technology Projects (2014B010117001, 2014A010103022, 2014A01010 3008, 2013B090200021, 2013B010202001); Fundamental Research Funds for the Central Universities, SCUT (2015ZZ098)

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    Abstract:

    Cloud computing is leading a revolution in computer science. However at the meantime, the large-scale ecosystem of cloud infrastructure brings about the problem of huge energy consumption. Therefore, the energy consumption management has been a research hotspot in recent years and to a large extent, the sustainable development of cloud computing has been tightly associated with the techniques of energy consumption measurement and management. Furthermore, the technique of power measurement in cloud environment is the foundation for the building of energy models as well as the evaluation of resource scheduling algorithms. In this paper, based on a survey of a wide range of methods measuring energy consumption of VMs, hosts or the whole system, four effective approaches that are widely applied in cloud system are addressed. The approaches include direct measuring techniques based on hardware or software, energy consumption estimate based on energy model, energy consumption measurement under virtualized environment, and energy consumption assessment based on simulation technology respectively. The paper analyzes and compares the advantages, drawbacks and best-fit situations of these methods. In addition, it discusses and points out the trends of future researches on energy management. These trends include smart power supply module, application type oriented energy consumption models, energy consumption model designed for mixed workloads, efficient cloud simulation toolkit for dynamic management, energy management in dynamic heterogeneous distributed cluster, energy preservation with resources scheduling towards tasks processing big data, and power provision scheduling with green energy.

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林伟伟,吴文泰.面向云计算环境的能耗测量和管理方法.软件学报,2016,27(4):1026-1041

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History
  • Received:September 29,2014
  • Revised:July 31,2015
  • Adopted:
  • Online: January 07,2016
  • Published:
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