计算机科学 ›› 2015, Vol. 42 ›› Issue (12): 26-31.

• 第十三届全国软件与应用学术会议 • 上一篇    下一篇

功耗感知的自适应粒子群优化虚拟机动态映射

苏宇,高 阳,秦志光   

  1. 南京大学计算机软件新技术国家重点实验室 南京210093;电子科技大学信息与软件工程学院 成都610054,南京大学计算机软件新技术国家重点实验室 南京210093,电子科技大学信息与软件工程学院 成都610054
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金重点项目(61432008,6),国家自然科学基金项目(61375121,9),国家自然科学基金联合项目(U1435214,U1401257),国家科技重大专项(20112X03002-002-03),国家高技术研究发展863计划(2011AA010706)资助

Power-aware Virtual Machine Dynamic Mapper Using Particle Swarm Optimization

SU Yu, GAO Yang and QIN Zhi-guang   

  • Online:2018-11-14 Published:2018-11-14

摘要: 功耗管理是云计算数据中心的重要问题之一。由于服务器在不同睡眠状态时的功耗及唤醒延迟不同,将空闲服务器节电状态与输入作业负载建立映射,设计并实现了一种新的元启发式调度器,利用适应粒子群优化(SAPSO)检测和跟踪云计算资源池中不断变化的最优目标服务器,考虑了资源动态、工作服务器不同负载时的功耗、空闲服务器不同休眠状态转换时的功耗,使得VM映射中功耗增量最小。仿真实验表明了所提方法的有效性和较好的性能,经比较分析可知,该方法在保证满足SLA的情况下最大限度地减少了功耗且提高了VM映射效率。

关键词: 虚拟机映射,功率感知,粒子群优化,云计算

Abstract: Power consumption management has become one of important issues in cloud computing.This paper designed and implemented a new meta-heuristic scheduler,in which the proposed self-adaptive particle swarm optimization (SAPSO) is used to detect and track the changing optimal target servers for virtual machine(VM) provisioning in the resource pool.The method considers resource dynamic and the power consumption of busy ser-vers with different loads as well as idle servers in different sleep states,minimizing the incremental power in VM mapping due to workload mapping without degrading performance.Simulations show that the proposed power aware SAPSO is significantly effective to minimize the power consumption increment without compromising both the performances of the VM mapping and QoS with SLA to cloud users.

Key words: VM mapping,Power-aware,Particle swarm optimization (PSO),Cloud computing

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