Metaheuristics for the Virtual Machine Mapping Problem in Clouds

Article type: Research Article

Authors: Nesmachnow, Sergio^{1; *} | Iturriaga, Santiago¹ | Dorronsoro, Bernabé² | Talbi, El-Ghazali³ | Bouvry, Pascal⁴

Affiliations: [1] Centro de Cálculo, Universidad de la República, Uruguay | [2] Department of Computer Science, University of Cádiz, Spain | [3] LIFL, University of Lille 1, France | [4] FTCS, University of Luxembourg, Luxembourg. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]

Correspondence: [*] Corresponding author.

Abstract: This article presents sequential and parallel metaheuristics to solve the virtual machines subletting problem in cloud systems, which deals with allocating virtual machine requests into pre-booked resources from a cloud broker, maximizing the broker profit. Three metaheuristic are studied: Simulated Annealing, Genetic Algorithm, and hybrid Evolutionary Algorithm. The experimental evaluation over instances accounting for workloads and scenarios using real data from cloud providers, indicates that the parallel hybrid Evolutionary Algorithm is the best method to solve the problem, computing solutions with up to 368.9% profit improvement over greedy heuristics results while accounting for accurate makespan and flowtime values.

Keywords: cloud computing, planning, brokering

Journal: Informatica, vol. 26, no. 1, pp. 111-134, 2015