An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers to save energy. Inspired by the promising perfo...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 22; H. 1; S. 113 - 128
Hauptverfasser:	Liu, Xiao-Fang, Zhan, Zhi-Hui, Deng, Jeremiah D., Li, Yun, Gu, Tianlong, Zhang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.02.2018
Schlagworte:	Algorithm design and analysis Ant colony system (ACS) Cloud computing Energy consumption Energy efficiency Genetic algorithms Servers virtual machine placement (VMP) Virtual machining
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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Abstract	Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers to save energy. Inspired by the promising performance of the ant colony system (ACS) algorithm for combinatorial problems, an ACS-based approach is developed to achieve the VMP goal. Coupled with order exchange and migration (OEM) local search techniques, the resultant algorithm is termed an OEMACS. It effectively minimizes the number of active servers used for the assignment of virtual machines (VMs) from a global optimization perspective through a novel strategy for pheromone deposition which guides the artificial ants toward promising solutions that group candidate VMs together. The OEMACS is applied to a variety of VMP problems with differing VM sizes in cloud environments of homogenous and heterogeneous servers. The results show that the OEMACS generally outperforms conventional heuristic and other evolutionary-based approaches, especially on VMP with bottleneck resource characteristics, and offers significant savings of energy and more efficient use of different resources.
AbstractList	Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers to save energy. Inspired by the promising performance of the ant colony system (ACS) algorithm for combinatorial problems, an ACS-based approach is developed to achieve the VMP goal. Coupled with order exchange and migration (OEM) local search techniques, the resultant algorithm is termed an OEMACS. It effectively minimizes the number of active servers used for the assignment of virtual machines (VMs) from a global optimization perspective through a novel strategy for pheromone deposition which guides the artificial ants toward promising solutions that group candidate VMs together. The OEMACS is applied to a variety of VMP problems with differing VM sizes in cloud environments of homogenous and heterogeneous servers. The results show that the OEMACS generally outperforms conventional heuristic and other evolutionary-based approaches, especially on VMP with bottleneck resource characteristics, and offers significant savings of energy and more efficient use of different resources.
Author	Liu, Xiao-Fang Deng, Jeremiah D. Li, Yun Zhan, Zhi-Hui Zhang, Jun Gu, Tianlong
Author_xml	– sequence: 1 givenname: Xiao-Fang surname: Liu fullname: Liu, Xiao-Fang organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 2 givenname: Zhi-Hui orcidid: 0000-0003-0862-0514 surname: Zhan fullname: Zhan, Zhi-Hui email: zhanapollo@163.com organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 3 givenname: Jeremiah D. surname: Deng fullname: Deng, Jeremiah D. organization: Department of Information Science, University of Otago, Dunedin, New Zealand – sequence: 4 givenname: Yun orcidid: 0000-0002-6575-1839 surname: Li fullname: Li, Yun organization: School of Computer Science and Network Security, Dongguan University of Technology, Dongguan, China – sequence: 5 givenname: Tianlong surname: Gu fullname: Gu, Tianlong organization: School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin, China – sequence: 6 givenname: Jun surname: Zhang fullname: Zhang, Jun email: junzhang@ieee.org organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
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Snippet	Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to...
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StartPage	113
SubjectTerms	Algorithm design and analysis Ant colony system (ACS) Cloud computing Energy consumption Energy efficiency Genetic algorithms Servers virtual machine placement (VMP) Virtual machining
Title	An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing
URI	https://ieeexplore.ieee.org/document/7750592
Volume	22
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