Journal of Jilin University(Engineering and Technology Edition) ›› 2018, Vol. 48 ›› Issue (6): 1859-1866.doi: 10.13229/j.cnki.jdxbgxb20170674

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Synchronous sleeping based energy saving strategy of reservation virtual machines in cloud data centers and its performance research

JIN Shun-fu1,2,3(),WANG Bao-shuai1,HAO Shan-shan1,JIA Xiao-guang1,HUO Zhan-qiang2()   

  1. 1. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004,China
    2. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000,China
    3. Science and Technology on Communication Networks Laboratory,Shijiazhuang 050081,China
  • Received:2017-06-27 Online:2018-11-20 Published:2018-12-11

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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

CLC Number: 

  • TP393

Fig.1

State transition of reservation group"

Fig.2

State transition of the system"

Table 1

System parameters"

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

Fig.3

Change trend for blocking probability of tasks"

Fig.4

Change trend for average response time of tasks"

Fig.5

Change trend for the energy saving rate of system"

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