一种面向数据中心的能耗感知虚拟机放置策略

doi:10.19665/j.issn1001-2400.2022.05.017

摘要/Abstract

摘要：

随着互联网的不断发展,数据中心规模不断扩大,其面临的突出问题是如何保证数据中心安全运行并降低其运行能耗。目前的研究中仅着眼于降低数据中心运行能耗,并未考虑服务器的环境温度。若在高温区域持续增加负载,则可能导致局部热点问题,使得制冷设备处于过度制冷状态而导致数据中心运行能耗整体提高。针对上述问题,提出一种能耗感知的虚拟机放置策略,可以在降低数据中心运行能耗条件下避免热点出现。策略由两部分算法组成:第1部分为最佳适应算法,算法将物理机序列按照可用的CPU资源大小进行排序,对于当前虚拟机请求,按照文中提出的温度迫切值计算方法选择迫切值最小的物理机作为目标位置,并将目标物理机序列二进制化后作为遗传算法的初始种群;在第2部分遗传算法中,对种群进行交叉变异操作,通过适应度函数计算的适应度值选择出下一代种群,不断迭代计算最终得出最优解。为了验证所提出策略的有效性,在cloudsim仿真计算平台上进行了相关实验。仿真结果表明,所提方法在降低运行能耗的同时也降低了服务器间的温度波动值从而避免热点出现。

关键词: 数据中心, 运行能耗, 遗传算法, 虚拟机放置

Abstract:

With the development of the Internet,the scale of the data centers continues to expand,with the prominent problem being how to ensure the safe operation of data centers and reduce the operation energy consumption.The current research focuses only on reducing the energy consumption of the data center,but does not consider the ambient temperature of the servers.If the load continues to increase in the high temperature area,it may lead to local hot spot problems and cause the refrigeration equipment to be in the over-cooling state,resulting in the overall increase of the energy consumption.To solve this problem,this paper proposes an energy-aware virtual machine placement strategy that can avoid hot spots while reducing the energy consumption of the data center.The strategy consists of two parts of the algorithm.The first part is the best adaptation algorithm which sorts the physical machine sequence according to the available CPU resource size.For the current virtual machine request,the physical machine with the smallest urgent value is selected as the target location according to the calculation method of temperature urgent value proposed in this paper,and the sequence of the target physical machine is binarized as the initial population of the genetic algorithm.In the second part of the genetic algorithm,the population is cross-mutated,the next-generation population is selected through the fitness value calculated by the fitness function,and the algorithm finally obtains the optimal solution through continuous iterative calculations.To verify the effectiveness of the strategy proposed in this paper,corresponding experiments are carried out on the cloudsim simulation computing platform.The simulation results show that the proposed method reduces not only the operating energy consumption but also the temperature fluctuation value between the servers to avoid the occurrence of hot spots.

Key words: data center, energy consumption, genetic algorithm, virtual machine placement

中图分类号:

TP399

杨傲, 马春苗, 伍卫国, 王思敏, 赵坤. 一种面向数据中心的能耗感知虚拟机放置策略[J]. 西安电子科技大学学报, 2022, 49(5): 145-153.

YANG Ao, MA Chunmiao, WU Weiguo, WANG Simin, ZHAO Kun. Energy-awarevirtual machine placement strategy for data centers[J]. Journal of Xidian University, 2022, 49(5): 145-153.

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配置	CPU/GHz	RAM/GB	带宽/(B·s^-1)
S₁	1	1	10 000
S₂	2	3	15 000
S₃	3	4	20 000

配置	CPU/GHz	RAM/GB	带宽/(B·s^-1)
V₁	0.5	1	3 000
V₂	1.0	2	5 000
V₃	2.0	2	10 000

Number of VMs	算法
Number of VMs	FF	SCA	Random	GA	Propose	最佳值
5	436.0	353.5	453.0	353.5	353.5	353.5
6	456.0	352.0	463.0	352.0	352.0	352.0
7	579.0	455.0	590.0	455.0	455.0	455.0
8	596.0	490.0	586.0	490.0	490.0	490.0
10	602.5	588.0	652.0	588.0	588.0	588.0

配置	CPU/GHz	RAM/GB	带宽/(B·s^-1)
S₁	1.86	4	30 000
S₂	2.06	4	30 000
S₃	2.46	4	30 000
S₄	2.66	6	30 000
S₅	2.96	8	30 000
S₆	3.16	8	30 000

配置	CPU/GHz	RAM/GB	带宽/(B·s^-1)
V₁	0.5	1.8	1.8
V₂	0.5	2.0	2.0
V₃	0.8	2.0	2.0
V₄	1.2	3.2	3.2
V₅	1.8	4.0	4.0
V₆	2.0	4.3	4.3