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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Vydáno v:Applied intelligence (Dordrecht, Netherlands) Ročník 43; číslo 2; s. 296 - 307
Hlavní autoři: Sait, Sadiq M., Shahid, Kh. Shahzada
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.09.2015
Springer Nature B.V
Témata:
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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Shrnutí: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.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-014-0634-x