Hybrid algorithms for placement of virtual machines across geo-separated data centers

Fernando Stefanello (), Vaneet Aggarwal (), Luciana S. Buriol () and Mauricio G. C. Resende ()
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Fernando Stefanello: Federal University of Rio Grande do Sul
Vaneet Aggarwal: Purdue University
Luciana S. Buriol: Federal University of Rio Grande do Sul
Mauricio G. C. Resende: Amazon.com

Journal of Combinatorial Optimization, 2019, vol. 38, issue 3, No 8, 748-793

Abstract: Abstract Cloud computing has emerged as a new paradigm for hosting and supplying services over the Internet. This technology has brought many benefits, such as eliminating the need for maintaining expensive computing hardware. With an increasing demand for cloud computing, providing performance guarantees for applications that run over cloud become important. Applications can be abstracted into a set of virtual machines with certain guarantees depicting the quality of service of the application. In this paper, we consider the placement of these virtual machines across multiple data centers (VMPlacement), meeting the quality of service requirements while minimizing the bandwidth cost of the data centers. This problem is a generalization of the NP-hard generalized quadratic assignment problem (GQAP). In this paper, we present a greedy randomized adaptive search procedure and a biased random-key genetic algorithm, both hybridized with a path-relinking strategy and a local search based on variable neighborhood descent for solving this problem. The hybrid heuristics are also tested on instances of the GQAP. We show that both algorithms are effective in quickly solving small and large instances of VMPlacement problem, especially when the path-relinking is used. For GQAP, the results outperform the previous state-of-the-art algorithms.

Keywords: Combinatorial optimization; Cloud computing; Biased random-key genetic algorithm; GRASP; Path-relinking (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10878-019-00411-3

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