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一种能量感知的虚拟机放置智能优化算法 被引量：5

Energy-aware intelligent optimization algorithm for virtual machine replacement
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摘要 针对数据中心的虚拟机放置优化问题,首先对异构数据中心的能耗进行建模,然后提出了一种基于离散粒子群优化的能量感知的虚拟机放置智能优化算法.重新定义了粒子的参数及算子,提出了一种二维编码方法,引入了一种自适应权重机制和一种能耗感知的局部适应优先机制以分别更新粒子的速度和位置,有效地提高了求解质量.仿真实验对比表明,本算法大大提高了数据中心服务器资源的平均利用率,减少了能耗,降低了数据中心的运营成本. 针对数据中心的虚拟机放置优化问题,首先对异构数据中心的能耗进行建模,然后提出了一种基于离散粒子群优化的能量感知的虚拟机放置智能优化算法.重新定义了粒子的参数及算子,提出了一种二维编码方法,引入了一种自适应权重机制和一种能耗感知的局部适应优先机制以分别更新粒子的速度和位置,有效地提高了求解质量.仿真实验对比表明,本算法大大提高了数据中心服务器资源的平均利用率,减少了能耗,降低了数据中心的运营成本.
作者 刘志飘 王尚广 孙其博 杨放春
机构地区 北京邮电大学网络与交换技术国家重点实验室
出处 《华中科技大学学报（自然科学版）》 EI CAS CSCD 北大核心 2012年第S1期398-402,共5页 Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金 国家自然科学基金资助项目(61272521) 教育部博士学科点专项科研基金优先发展领域基金资助项目(20110005130001) 教育部新世纪优秀人才计划资助项目(100263)
关键词 能量感知 粒子群优化 虚拟机 多维装箱 二维编码 局部适应优先 energy aware particle swarm optimization virtual machine multi-dimension bin packing two dimension encoding local fit first
分类号 N55 [自然科学总论]
  • 相关文献

参考文献13

  • 1王文彬,孙其博,赵新超,杨放春.基于非均衡变异离散粒子群算法的QoS全局最优Web服务选择方法[J].电子学报,2010,38(12):2774-2779. 被引量：24
  • 2Urgaonkar B,Rosenberg A,Shenoy P.Applicationplacement on a cluster of servers. InternationalJournal on Foundations of Computer Science . 2007
  • 3Gunjan K,Kirk B,Gautam K,et al.Applicationperformance management in virtualized server envi-ronments. Proceedings of the IEEE/IFIPNOMS′06 . 2006
  • 4Anton B,Rajkumar B.Optimal online deterministical gorithms and adaptive heuristics for energy andperformance efficient dynamic consolidation of virtu-al machines in cloud data centers. Concurrencyand Computation:Practice and Experience . 2011
  • 5Chen M,Zhangy H,Ya-Yunn S et al.Effective VM sizing in virtualized data centers. Proceedings of the12th IFIP/IEEE International Symposium on Integrated Network Manage-ment (IM’’2011) . 2011
  • 6Dasgupta G,Sharma A,Verma A,Neogi A,Kothari R.Workload management for power efficiency in virtualized da-ta-centers. Communications of the ACM . 2011
  • 7Chen G,He W,Liu J, et al.Energy-aware server provisioning and load dispatching for connection-intensive internet services. Networked Systems Design & Implemen-tation . 2008
  • 8Beloglazov A,Abawajy J,Buyya R.Energy-aware resourceallocation heuristics for efficient management of data centers forcloud computing. Future Generation Computer Systems . 2012
  • 9S. Agrawal,S.K. Bose,S. Sundarrajan.Grouping genetic algorithm for solving the serverconsolidation with conflicts. Proceedings of the1st ACM/SIGEVO Summit on Genetic andEvolutionary Computation . 2009
  • 10Tang C,Steinder M,Spreitzer M,et al.A scalable application placement controller for enterprise data centers. Proceedings of the16th international conference on World Wide Web,WWW’’07 . 2007

二级参考文献4

共引文献23

同被引文献38

  • 1Armbmst M, Fox A, Griffith R, et al. A view of cloud computing[J]. Communications of the Association for Computing Machiner- y,2010,53 (4) :50-58.
  • 2Buyya R, Yeo C S, Venugopal S, et al. Cloud computing and emer- ging 1T platforms: Vision, hype, and reality for delivering compu- ting as the 5th utility [ J]. Future Generation Computer Systems, 2009,25 (6) :599-616.
  • 3Zhu X,Young D,Watson B J,et al. 1000 islands:an integrated ap- proach to resource management for virtualized data centers [ J ]. Cluster Computing,2009,12 ( 1 ) :45-57.
  • 4Beloglazov A,Ahawajy J, Buyya R. Energy-aware resource alloca- tion heuristics for efficient management of data centers for cloud computing [ J ]. Future Cncration Computer Systems, 2012, 28 (5) :755-768.
  • 5Liu Zhi-piao, Wang Shang-guang, Sun Qi-bo, et al. Energy-aware intelligent optimization algorithm for virtual machine replacement [ J ]. Journal of Huazhong University of Science & Technology ( Natural Science Edition), 2012,12 ( 40 ) : 398 -402.
  • 6Srikantaiah S, Kansal A, Zhao F. Energy aware consolidation for cloud computing[ C]. Proceedings of the 2008 Conference on Pow- er Aware Computing and Systems, USENIX Association,2008.
  • 7Ajiro Y, Tanaka A. Improving packing algorithms for server consol- idation[ C]. Proceedings of International Conference for the Com- putere Measurement Group (CMG) ,2007:399406.
  • 8Gupta R,Bose S K,Sundarrajan S,et al. A two stage heuristic al- gorithm for solving the server consolidation problem with item-item and bin-item incompatibility constraints [ C ]. Proceedings of the 2008 Institute of Electrical and Electronics Engineers International Conference on Services Computing (SCC'08) ,2008,2:39-46.
  • 9Agrawal S ,Bose S K, Sundarrajan S. Grouping genetic algorithm for solving the serverconsolidation problem with conflicts [ C ]. Proceed- ings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation,Association for Computing Machinery ,2009:1-8.
  • 10Wood T, Shenoy P J, Venkataramani A, et al. Black-box and Gray- box strategies for virtual machine migration [ C ]. Proceedings of Symposium on Networked Systems Design and Implementation (NSDI) ,2007,7:229-242.

引证文献5

二级引证文献27

华中科技大学学报（自然科学版）
2012年 第S1期

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