Energy-Efficiency Model and Measuring Approach for Cloud Computing
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    Abstract:

    This paper presents an EE (energy efficiency) model and measuring approach for cloud computing. A mathematical expression of EE is first defined, as well as the measuring and calculation approaches, and the extreme conditions of EE are deduced. Then, to facilitate calculating EE, the mathematical expression between the computer power and the CPU working state is improved, thus EE can be calculated through CPU usage and CPU frequency, which simplifies the measurement of EE. In addition, the study designs and implements a series of experiments to verify the correctness of the proposed model. CPU-intensive, I/O intensive and interactive jobs are performed in both stand-alone environment and cloud environment to evaluate their EEs and to summarize their features as well as the optimization approaches. Both the theory and experiments have proved that the proposed EE model and measuring approach can accurately evaluate the EE of cloud systems and lay the foundation for EE optimization.

    Reference
    [1] Chen G, He WB, Liu J, Nath S, Rigas L, Xiao L, Zhao F. Energy-Aware server provisioning and load dispatching for connectionintensive Internet services. In: Crowcroft J, Dahlin M, eds. Proc. of the 5th USENIX Symp. on Networked Systems Design and Implementation (NSDI). San Francisco: USENIX Association, 2008. 337-350.
    [2] Urgaonkar B, Shenoy PJ, Chandra A, Goyal P, Wood T. Agile dynamic provisioning of multi-tier Internet applications. Trans. on Autonomous and Adaptive Systems, 2008,3(1):1-39. [doi: 10.1145/1342171.1342172]
    [3] Orgerie AC, Lef?vre L, Gelas JP. Save Watts in your grid: Green strategies for energy-aware framework in large scale distributed systems. In: Proc. of the 14th Int’l Conf. on Parallel and Distributed Systems (ICPADS 2008). Melbourne: IEEE, 2008. 171-178. [doi: 10.1109/ICPADS.2008.97]
    [4] IBM project big green. http://www-03.ibm.com/press/us/en/pressrelease/21524.wss
    [5] Using virtualization to improve data center efficiency. http://www.thegreengrid.org/Global/Content/white-papers/Using-Virtualization-to-Improve-Data-Center-Efficiency
    [6] Rivoire S, Shah MA, Ranganathan P, Kozyrakis C. JouleSort: A balanced energy-efficiency benchmark. In: Chan CY, Qoi BC, Zhou A, eds. Proc. of the ACM SIGMOD Int’l Conf. on Management of Data. Beijing: ACM Press, 2007. 365-376. [doi: 10.1145/1247480.1247522]
    [7] Bahsoon R. Green cloud: Towards a framework for dynamic self-optimization of power and dependability requirements in green cloud architectures. In: Babar MA, Gorton I, eds. Proc. of the 4th European Conf. on Software Architecture (ECSA 2010). Copenhagen, 2010. 510-514.
    [8] Kumar K, Lu YH. Cloud computing for mobile users: Can offloading computation save energy? IEEE Computer, 2010,43(4): 51-56. [doi: 10.1109/MC.2010.98]
    [9] Kelenyi I, Nurminen JK. CloudTorrent—Energy-Efficient BitTorrent content sharing for mobile devices via cloud services. In: Proc. of the 7th IEEE on Consumer Communications and Networking Conf. (CCNC). 2010. 1-2.
    [10] Elnozahy EN, Kistler M, Rajamony R. Energy-Efficient server clusters. In: Falsafi B, Vijaykumar TN, eds. Proc. of the 2nd Int’l Workshop on Power-Aware Computer Systems (PACS 2002). Cambridge: Springer-Verlag, 2003. 179-197. [doi: 10.1007/3-540-36612-1_12]
    [11] Abdelsalam HS, Maly K, Mukkamala R, Zubair M, Kaminsky D. Analysis of energy efficiency in clouds. In: Proc. of Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns (COMPUTATIONWORLD 2009). 2009. 416-421. [doi: 10.1109/ComputationWorld.2009.38]
    [12] Lef?vre L, Orgerie AC. Designing and evaluating an energy efficient cloud. The Journal of Supercomputing, 2010,51(3):352-373. [doi: 10.1007/s11227-010-0414-2]
    [13] Orgerie AC, Lef?vre L. When clouds become green: The green open cloud architecture. In: Proc. of the Int’l Conf. on Parallel Computing: From Multicores and GPU’s to Petascale. 2010. 228-237.
    [14] Li B, Li JX, Huai JP, Wo TY, Li Q, Zhong L. EnaCloud: An energy-saving application live placement approach for cloud computing environments. In: Proc. of the IEEE Int’l Conf. on Cloud Computing (CLOUD 2009). Bangalore: IEEE, 2009. 17-24. [doi: 10.1109/CLOUD.2009.72]
    [15] Younge AJ, von Laszewski G, Wang LZ, Lopez-Alarcon S, Carithers W. Efficient resource management for cloud computing environments. In: Proc. of the Int’l Green Computing Conf. Chicago: IEEE, 2010. 357-364. [doi: 10.1109/GREENCOMP.2010.5598294]
    [16] Srikantaiah S, Kansal A, Zhao F. Energy aware consolidation for cloud computing. In: Proc. of the 2008 Conf. on Power Aware Computing and Systems. Berkeley: USENIX Association, 2008. 10-14.
    [17] Lee YC, Zomaya AY. Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans. on Parallel and Distributed Systems, 2011,22(8):1374-1381. [doi: 10.1109/TPDS.2010.208]
    [18] Chen YY, Das A, Qin WB, Sivasubramaniam A, Wang Q, Gautam N. Managing server energy and operational costs in hosting centers. In: Eager DL, Williamson CL, Borst SC, Lui JCS, eds. Proc. of the Int’l Conf. on Measurements and Modeling of Computer Systems (SIGMETRICS). Banff: ACM Press, 2005. 303-314. [doi: 10.1145/1064212.1064253]
    [19] GridMix program. Hadoop source distribution: src/benchmarks/gridmix
    [20] Jia Y, Shao Z: A benchmark for hive, PIG and hadoop. http://issues.apache.org/jira/browse/HIVE-396
    [21] Sort program. Hadoop source distribution: src/examples/org/apache/hadoop/examples/sort
    [22] Huang SS, Huang J, Dai JQ, Xie T, Huang B. The HiBench benchmark suite: Characterization of the mapreduce-based data analysis. In: Proc. of the 26th Int’l Conf. on Data Engineering Workshops (ICDEW). Long Beach: IEEE, 2010. 41-51. [doi: 10.1109/ICDEW.2010.5452747]
    [23] DFSIO program. Hadoop source distribution: src/test/org/apache/hadoop/fs/TestDFSIO
    [24] Module for Monte Carlo Pi. http://math.fullerton.edu/mathews/n2003/montecarlopimod.html
    [25] WordCount program. Hadoop source distribution: src/examples/org/apache/hadoop/examples/WordCount
    [26] MRBench program. Hadoop source distribution: src/examples/org/apache/hadoop/mapred/MRBench
    [27] MRBench: Setting the baseDir parameter has no effect. https://issues.apache.org/jira/browse/MAPREDUCE-2398
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宋杰,李甜甜,闫振兴,那俊,朱志良.一种云计算环境下的能效模型和度量方法.软件学报,2012,23(2):200-214

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History
  • Received:July 17,2011
  • Revised:September 06,2011
  • Online: February 07,2012
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