Energy-efficient virtual machine placement in data centers by genetic algorithm
Data(s) |
2012
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Resumo |
Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines in a data center only, but do not consider the energy consumption in communication network in the data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement in order to make the data center more energy-efficient. In this paper, we propose a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both the servers and the communication network in the data center. Experimental results show that the genetic algorithm performs well when tackling test problems of different kinds, and scales up well when the problem size increases. |
Formato |
application/pdf |
Identificador | |
Publicador |
Springer Berlin Heidelberg |
Relação |
http://eprints.qut.edu.au/53767/1/246.pdf DOI:10.1007/978-3-642-34487-9_39 Wu, Grant, Tang, Maolin, Tian, Yu-Chu, & Li, Wei (2012) Energy-efficient virtual machine placement in data centers by genetic algorithm. In Lecture Notes on Computer Science, Springer Berlin Heidelberg, Renaissance Doha City Center Hotel, Doha, pp. 315-323. |
Direitos |
Copyright 2012 Springer The final publication is available at link.springer.com |
Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Palavras-Chave | #080108 Neural Evolutionary and Fuzzy Computation #080599 Distributed Computing not elsewhere classified #Server consolidation #Virtual machine placement #Data center #optimization #Genetic algorithm |
Tipo |
Conference Paper |