基于备用虚拟机同步休眠的云数据中心节能策略及性能

doi:10.13229/j.cnki.jdxbgxb20170674

摘要/Abstract

摘要：

为了降低能源消耗实现绿色云计算,提出一种基于备用虚拟机同步休眠的云数据中心节能策略。系统中的虚拟机分为主模块和备用模块两大模块,其中,主模块一直处于活跃状态;根据云数据中心的工作负载轻重,备用模块处于休眠状态或者活跃状态。考虑到云数据中心可以为多任务用户请求提供服务,建立了一个批量到达且部分服务台同步多重休假的排队模型。采用高斯赛德尔法,求解系统模型的稳态分布,给出系统节能率等性能指标表达式。最后,进行数值试验和仿真试验,研究了基于备用虚拟机同步休眠的云数据中心节能策略的系统性能,并验证了所提策略的有效性。

关键词: 计算机应用, 云数据中心, 部分休眠, 批量到达, 高斯塞德尔法

Abstract:

With the rapid development of cloud computing, more and more people shift their workload to the cloud data centers, the energy consumption of the cloud data centers is non-negligible. In order to reduce energy consumption and achieve green cloud service, an energy saving strategy in the cloud data centers based on synchronous sleeping of the reservation virtual machines is proposed. All the Virtual Machines (VMs) are divided into two groups: the main group and the reservation group. The main group is always activated. According to the traffic load of the cloud data centers, the reservation group may be asleep or activated. Considering the cloud data centers served for the user requests with multiple tasks, a batch arrival queuing model with part servers synchronization multiple vacation is established. By using Gauss Seidel method, the steady-state distribution of the system model is derived, and the performance measures, such as the energy saving rate of system are evaluated. Numerical experiments with analysis and simulation are provided to study the system performance of the energy saving strategy in the cloud data centers based on synchronous sleeping of the reservation VMs and verify the effectiveness of the proposed strategy.

Key words: computer application, cloud data centers, partial sleep, batch arrival, Gauss Seidel method

中图分类号:

TP393

金顺福,王宝帅,郝闪闪,贾晓光,霍占强. 基于备用虚拟机同步休眠的云数据中心节能策略及性能[J]. 吉林大学学报(工学版), 2018, 48(6): 1859-1866.

JIN Shun-fu,WANG Bao-shuai,HAO Shan-shan,JIA Xiao-guang,HUO Zhan-qiang. Synchronous sleeping based energy saving strategy of reservation virtual machines in cloud data centers and its performance research[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1859-1866.

图/表 6

图1

图2

表1

系统参数"

参数	数值
主模块虚拟机的数量 $n$ /个	20
备用模块虚拟机的数量 $m$ /个	20
缓冲区容量 $r$ /个	70
用户请求规模参数 $θ$	0.2
虚拟机服务率 $μ$ /s^-1	1.0

表1

图3

图4

图5

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