DocumentCode :
9582
Title :
A Stochastic Approach to Analysis of Energy-Aware DVS-Enabled Cloud Datacenters
Author :
Yunni Xia ; Mengchu Zhou ; Xin Luo ; ShanChen Pang ; Qingsheng Zhu
Author_Institution :
Sch. of Comput., Chongqing Univ., Chongqing, China
Volume :
45
Issue :
1
fYear :
2015
fDate :
Jan. 2015
Firstpage :
73
Lastpage :
83
Abstract :
With the increasing call for green cloud, reducing energy consumption has been an important requirement for cloud resource providers not only to reduce operating costs, but also to improve system reliability. Dynamic voltage scaling (DVS) has been a key technique in exploiting the hardware characteristics of cloud datacenters to save energy by lowering the supply voltage and operating frequency. This paper presents a novel stochastic framework for energy efficiency and performance analysis of DVS-enabled cloud. This framework uses virtual machine request arrival rate, failure rate, repair rate, and service rate of datacenter servers as model inputs. Based on a queuing-network-based analysis, this paper gives analytic solutions of three metrics. The proposed framework can be used to help the design and optimization of energy-aware high performance cloud systems.
Keywords :
cloud computing; computer centres; energy consumption; file servers; green computing; power aware computing; virtual machines; datacenter servers; dynamic voltage scaling; energy consumption reduction; energy efficiency; energy-aware DVS-enabled cloud datacenters; energy-aware high performance cloud systems; failure rate; green cloud; operating cost reduction; operating frequency; repair rate; service rate; stochastic approach; supply voltage; system reliability; virtual machine request arrival rate; Analytical models; Computers; Energy consumption; Maintenance engineering; Markov processes; Voltage control; Cloud; dynamic voltage scaling (DVS); energy efficiency; performance;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics: Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2168-2216
Type :
jour
DOI :
10.1109/TSMC.2014.2331022
Filename :
6870491
Link To Document :
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