Engineering Simulated Evolution for Virtual Machine Assignment Problem

Cloud computing is a rapidly growing services business in today’s IT market. Its sharp growth is producing many challenges for cloud managers. One primary concern is to efficiently manage the cloud resources, i.e., to maximize utilization of hardware with minimum power consumption. Virtual Machine (...

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Vydané v:	Applied intelligence (Dordrecht, Netherlands) Ročník 43; číslo 2; s. 296 - 307
Hlavní autori:	Sait, Sadiq M., Shahid, Kh. Shahzada
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.09.2015 Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Assignment problem Cloud computing Computer centers Computer engineering Computer peripherals Computer Science Cost control Demand Energy consumption Hardware Heuristic Intelligence Load Machines Manufacturing Markets Mathematical models Mechanical Engineering Operating systems Optimization Packing problem Processes Searching Simulated evolution Software services Virtual environments Workloads Nondeterministic algorithms NP-hard Evolutionary metaheuristic Combinatorial optimization Virtual machine placement Simulated evolution
ISSN:	0924-669X, 1573-7497
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Abstract	Cloud computing is a rapidly growing services business in today’s IT market. Its sharp growth is producing many challenges for cloud managers. One primary concern is to efficiently manage the cloud resources, i.e., to maximize utilization of hardware with minimum power consumption. Virtual Machine (VM) consolidation is a very helpful approach to achieve these goals. In this context, we investigate the VM assignment problem. We describe the engineering of a nondeterministic iterative heuristic known as Simulated Evolution (SimE) to solve the well-known NP-hard problem of assigning VMs to hardware hosts. A ‘goodness’ function which is related to the target objective of the problem is defined. It guides the moves and helps traverse the search space in an intelligent manner. In the process of evolution, VMs with high goodness value have a smaller probability of getting perturbed, while those with lower goodness value may be reallocated via a compound move. Results are compared with those published in previous studies, and it is found that the proposed approach is efficient both in terms of solution quality and computational time demand.
AbstractList	Cloud computing is a rapidly growing services business in today's IT market. Its sharp growth is producing many challenges for cloud managers. One primary concern is to efficiently manage the cloud resources, i.e., to maximize utilization of hardware with minimum power consumption. Virtual Machine (VM) consolidation is a very helpful approach to achieve these goals. In this context, we investigate the VM assignment problem. We describe the engineering of a nondeterministic iterative heuristic known as Simulated Evolution (SimE) to solve the well-known NP-hard problem of assigning VMs to hardware hosts. A 'goodness' function which is related to the target objective of the problem is defined. It guides the moves and helps traverse the search space in an intelligent manner. In the process of evolution, VMs with high goodness value have a smaller probability of getting perturbed, while those with lower goodness value may be reallocated via a compound move. Results are compared with those published in previous studies, and it is found that the proposed approach is efficient both in terms of solution quality and computational time demand.
Author	Shahid, Kh. Shahzada Sait, Sadiq M.
Author_xml	– sequence: 1 givenname: Sadiq M. surname: Sait fullname: Sait, Sadiq M. email: sadiq@kfupm.edu.sa organization: Department of Computer Engineering and Center for Communications and IT Research, Research Institute King Fahd University of Petroleum & Minerals – sequence: 2 givenname: Kh. Shahzada surname: Shahid fullname: Shahid, Kh. Shahzada organization: Department of Computer Engineering, King Fahd University of Petroleum & Minerals
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Cites_doi	10.1016/j.jcss.2013.02.004 10.1145/37888.37897 10.1109/INFCOM.2012.6195716 10.1016/j.cie.2011.07.023 10.1002/3527606734 10.1016/j.eswa.2013.03.037
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References_xml	– reference: Jhawar R, Piuri V, Samarati P (2012). In: CSE, pp. 170–177 – reference: Raghavendra R, Ranganathan P , Talwar V, Wang Z, Zhu (2008). In: ACM SIGARCH Computer Architecture News, vol. 36 (ACM, 2008), vol. 36, pp. 48–59 – reference: Verma A, Dasgupta G, Nayak TK, De P, Kothari R (2009). In: Proceedings of the 2009 Conference on USENIX Annual Technical Conference (USENIX Association, 2009), pp. 28–28 – reference: SaitSMYoussefHVISI physical design automation: Theory and practice1994New YorkMcGraw-Hill – reference: Technology research - Gartner Inc. (2009). http://www.gartner.com/newsroom/id/1234513 – reference: GulatiAHollerAJiMShanmuganathanGWaldspurgerCZhuXVMware Tech J20121145 – reference: Bash CE , Patel CD , Sharma RK (2006). In: Thermal and Thermomechanical Phenomena in Electronics Systems, 2006. ITHERM’06. 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In: Parallel & Distributed Processing (IPDPS), IEEE International Symposium on (IEEE, 2010), pp. 1–11 – reference: Gartner report (2013). http://www.gartner.com/newsroom/id/2352816 – reference: Panigrahy R, Talwar K, Uyeda L, Wieder U (2011) Heuristics for vector bin packing. research. microsoft. com – reference: Breitgand D, Epstein A (2012) INFOCOM, . In: Proceedings IEEE (IEEE, 2012), pp. 2861–2865 – reference: Sait SM, Youssef H (1999) Iterative Computer Algorithms with Applications in Engineering: Solving Combinatorial Optimization Problems (IEEE Computer Society Press) – reference: Chen G, He W, Liu J, Nath S, Rigas L, Xiao L, Zhao F (2008). In: NSDI, vol. 8, pp. 337–350 – reference: Koomey J (2011) Growth in data center electricity use 2005 to 2010. A report by Analytical Press, completed at the request of The New York Times – reference: Kling RM, Banerjee P (1987). In: Proceedings of the 24th ACM/IEEE Design Automation Conference (ACM, 1987), pp. 60–66 – reference: Doddavula SK, Kaushik M, Jain A (2011) – reference: Xu J, Fortes JA (2010). In: Green Computing and Communications (GreenCom), 2010 IEEE/ACM Int’l Conference on & Int’l Conference on Cyber, Physical and Social Computing (CPSCom) (IEEE, 2010), pp. 179–188 – volume: 79 start-page: 1230 issue: 8 year: 2013 ident: 634_CR16 publication-title: J Comput Syst Sci doi: 10.1016/j.jcss.2013.02.004 – volume-title: VISI physical design automation: Theory and practice year: 1994 ident: 634_CR28 – ident: 634_CR17 – ident: 634_CR15 – ident: 634_CR13 – ident: 634_CR11 – ident: 634_CR7 – ident: 634_CR24 – ident: 634_CR5 – ident: 634_CR19 – ident: 634_CR9 – ident: 634_CR26 doi: 10.1145/37888.37897 – ident: 634_CR1 – ident: 634_CR3 – ident: 634_CR20 doi: 10.1109/INFCOM.2012.6195716 – ident: 634_CR10 – volume: 1 start-page: 45 issue: 1 year: 2012 ident: 634_CR14 publication-title: VMware Tech J – ident: 634_CR12 – ident: 634_CR8 – ident: 634_CR23 – ident: 634_CR18 – ident: 634_CR6 – ident: 634_CR2 – ident: 634_CR22 doi: 10.1016/j.cie.2011.07.023 – volume-title: Phase transitions in combinatorial optimization problems - Basics, Algorithms and Statistical Mechanics year: 2005 ident: 634_CR29 doi: 10.1002/3527606734 – ident: 634_CR27 – ident: 634_CR4 – ident: 634_CR25 – volume: 40 start-page: 5266 issue: 13 year: 2013 ident: 634_CR21 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2013.03.037
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SubjectTerms	Algorithms Artificial Intelligence Assignment problem Cloud computing Computer centers Computer engineering Computer peripherals Computer Science Cost control Demand Energy consumption Hardware Heuristic Intelligence Load Machines Manufacturing Markets Mathematical models Mechanical Engineering Operating systems Optimization Packing problem Processes Searching Simulated evolution Software services Virtual environments Workloads
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Title	Engineering Simulated Evolution for Virtual Machine Assignment Problem
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