云数据中心高能效的虚拟机迁移整合算法研究

李鸿健; 代字; 刘锐; 蒋溢

doi:10.11959/j.issn.1000-0801.2015033

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云数据中心高能效的虚拟机迁移整合算法研究

研究与开发 | 更新时间：2024-06-05

- 云数据中心高能效的虚拟机迁移整合算法研究
- Energy-Efficient Virtual Machine Migration and Consolidation Algorithm in Cloud Data Center
- 电信科学 2015年31卷第1期页码：65-71
- 作者机构：
- 作者简介：
  
  [ "李鸿健，男，博士，重庆邮电大学计算机学院副教授，主要研究方向为网络与高能性计算、云计算与大数据处理等。" ]
  [ "代字，男，重庆邮电大学计算机学院讲师，主要研究方向为神经网络、图像处理等。" ]
  [ "刘锐，男，重庆邮电大学计算机学院讲师，主要研究方向为云计算与虚拟化。" ]
  [ "蒋溢，男，重庆邮电大学计算机学院教授，主要研究方向为商业智能、数据挖掘等。" ]
- 基金信息：
  
  国家发展和改革委员会2012年物联网技术研发及产业化专项基金资助项目;2012 IOT Project of National Development and Reform Commission(ls201301);国家工业和信息化部2012年物联网发展专项基金资助项目;2012 IOT Project of National Ministry of Industry and Information Technology(D2013-47);重庆市教委科学技术研究基金资助项目;Scientific and Technological Research Program of Chongqing Municipal Education Commission(KJ130514)
- DOI：10.11959/j.issn.1000-0801.2015033
  中图分类号：
- 网络出版日期：2015-01，
  
  纸质出版日期：2015-01-20
- 稿件说明：
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李鸿健, 代字, 刘锐, 等. 云数据中心高能效的虚拟机迁移整合算法研究[J]. 电信科学, 2015,31(1):65-71.

Hongjian Li, Yu Dai, Rui Liu, et al. Energy-Efficient Virtual Machine Migration and Consolidation Algorithm in Cloud Data Center[J]. Telecommunications science, 2015, 31(1): 65-71.
李鸿健, 代字, 刘锐, 等. 云数据中心高能效的虚拟机迁移整合算法研究[J]. 电信科学, 2015,31(1):65-71. DOI： 10.11959/j.issn.1000-0801.2015033.

Hongjian Li, Yu Dai, Rui Liu, et al. Energy-Efficient Virtual Machine Migration and Consolidation Algorithm in Cloud Data Center[J]. Telecommunications science, 2015, 31(1): 65-71. DOI： 10.11959/j.issn.1000-0801.2015033.

摘要

基于多资源能效模型，通过改进CPU 双阈值法提出了多资源双阈值法触发虚拟机迁移，并将基于粒子群算法的虚拟机放置算法应用于虚拟机的能效整合。仿真实验结果表明，与传统的启发式算法相比，该算法有效地减少了物理节点的启用数量和虚拟机迁移次数，使系统资源利用率更加均衡。

Abstract

An energy-efficient virtual machine（VM）migration and consolidation algorithm with multi-resource was proposed. The algorithm was developed based on a multi-resource energy efficiency model. A method of multi-resource utilization double threshold was designed to trigger migration of virtual machine

and the optical particle swarm optimization method was introduced in the consolidation of virtual machine. Compared with traditional heuristic algorithms

the algorithm reduces the number of active physical nodes and the amount of VM's migration effectively

and obtains better energy efficiency in cloud data center.

关键词

Keywords

references

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Atefeh K , Saurabh K , Rajkumar B . Energy and carbon-efficient placement of virtual machines in distributed cloud data centers . Proceedings of the 19th International European Conference on Parallel and Distributed Computing , Aachen, Germany , 2013 ,

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Gandhi A , Harcho M , Das R , et al . Optimal power allocation in sever farms . Proceedings of the 11th International Joint Conference on Measurement and Modeling of Computer Systems , New York, USA , 2009 : 157 ～ 168

Chen G , He W , Liu J , et al . Energy-aware server provisioning and load dispatching for connection-intensive internet services . Proceedings of Symposium on Networked Systems Design and Implementation , California, USA , 2008 : 337 ～ 350

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Eberhart R , Kennedy J . A new optimizer using particle swarm theory . Proceedings of the 6th International Symposium on Micro Machine and Human Science , Nagoya, Japan , 1995 : 39 ～ 43

Kennedy J , Eberhart R C . A discrete binary version of the particle swarm algorithm . Proceedings of IEEE International Conference on Computational Cybernetics and Simulation , Piscataway, USA , 1997 : 4104 ～ 4108

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