Metaheuristics for the Virtual Machine Mapping Problem in Clouds

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 prebooked
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.