吉林大学学报(工学版) ›› 2020, Vol. 50 ›› Issue (1): 237-246.doi: 10.13229/j.cnki.jdxbgxb20180759

• 计算机科学与技术 • 上一篇    下一篇

基于新型休眠模式的云虚拟机分簇调度策略及性能优化

金顺福1(),郄修尘1,武海星1,霍占强2()   

  1. 1. 燕山大学 信息科学与工程学院，河北 秦皇岛 066004
    2. 河南理工大学 计算机科学与技术学院，河南 焦作 454000
  • 收稿日期:2018-06-26 出版日期:2020-01-01 发布日期:2020-02-06
  • 通讯作者: 霍占强 E-mail:jsf@ysu.edu.cn;hzq@hpu.edu.cn
  • 作者简介:金顺福(1966-)，女，教授，博士生导师．研究方向：计算机网络性能分析，排队论理论与应用.E-mail:jsf@ysu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61872311);河北省自然科学基金项目(F2017203141)

Clustered virtual machine allocation strategy in cloud computing based on new type of sleep-mode and performance optimization

Shun-fu JIN1(),Xiu-chen QIE1,Hai-xing WU1,Zhan-qiang HUO2()   

  1. 1. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
    2. College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China
  • Received:2018-06-26 Online:2020-01-01 Published:2020-02-06
  • Contact: Zhan-qiang HUO E-mail:jsf@ysu.edu.cn;hzq@hpu.edu.cn

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摘要：

针对云数据中心数量增加与规模扩张中存在的云计算能耗控制问题，引入了唤醒阈值与休眠定时器双重控制的周期性休眠模式，提出了一种云虚拟机分簇调度策略。云数据中心的虚拟机分为2个模块：模块I中的虚拟机时刻保持唤醒；模块II中的虚拟机则根据云数据中心的工作负载轻重在休眠状态与唤醒状态间切换。通过构建具有双速率与部分服务台异步(N,T)策略多重休假的排队模型，运用矩阵几何解方法，从云请求平均时延与系统能量节省率等方面评估云虚拟机分簇调度策略的系统性能。综合理论分析结果与仿真统计结果，验证了云虚拟机分簇调度策略的有效性。从经济学角度出发，构建了系统成本函数，引入了An混沌映射机制与非线性递减惯性权值策略，改进了粒子群智能优化算法，给出了策略参数的优化方案，实现了系统响应性能与节能效果之间的合理平衡。

关键词: 计算机应用, 云计算, 云数据中心, 虚拟机调度策略, 休眠模式, 排队模型, 粒子群优化算法

Abstract:

With the constant increase in the number and the scale of cloud data centers, the energy consumption control in cloud computing is becoming increasingly apparent. By introducing a periodic sleep mode with double control of wake up threshold and sleep timer, we propose a clustered virtual machine (VM) allocation strategy. All the VMs in a cloud data center are divided into two modules: The VMs in Module I are always awake, while the VMs in Module II will switch between sleep state and awake state according to the workload of the cloud data center. By establishing a queueing model with double service rates and (N,T) policy asynchronous multiple vacations of partial servers, and using the method of a matrix geometric solution, we evaluate the performance of the clustered VM allocation strategy in terms of average latency of cloud requests and energy saving rate of the system. Theoretical analysis results and simulation results verify the effectiveness of the proposed clustered VM allocation strategy. By constructing a system cost function from the perspective of economics, introducing an An chaotic mapping mechanism and a nonlinear decreasing inertia weight strategy to the Particle Swarm Optimization (PSO) algorithm, we optimize the strategy parameters to achieve a reasonable balance between the response performance and the energy efficiency of the system.

Key words: computer application, cloud computing, cloud data centers, virtual machine allocation strategy, sleep-mode, queueing model, particle swarm optimization(PSO) algorithm

中图分类号: 

  • TP393

图1

虚拟机的工作流程"

图2

云请求平均时延的变化趋势"

图3

系统能量节省率的变化趋势"

表2

模块II虚拟机数量与休眠参数的联合优化结果"

唤醒阈值 N 优化组合 ( d * , θ * ) 最小成本函数 F *
5 (22, 0.000 1) 4.877 9
15 (22, 0.000 2) 6.468 5

25

35

(21, 0.025 3)

(21, 0.027 5)

7.180 2

7.198 1
45 (21, 0.027 6) 7.199 1
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