An Experimental Comparison of Algorithms for Virtual Machine Placement Considering Many Objectives
Fabio López‐Pires, Benjamı́n Barán, Augusto Amarilla, Leonardo Benítez, Rodrigo Ferreira, Saúl Zalimben · 2016
Cloud computing datacenters provide thousands to millions of virtual machines (VMs) on-demand in highly dynamic environments, requiring quick placement of requested VMs into available physical machines (PMs). Due to the randomness of customer requests, the Virtual Machine Placement (VMP) should be formulated as an online optimization problem. This work presents a formulation of a VMP problem considering the optimization of the following objective functions: (1) power consumption, (2) economical revenue, (3) quality of service and (4) resource utilization. To analyze alternatives to solve the formulated problem, an experimental comparison of five different online deterministic heuristics against an offline memetic algorithm with migration of VMs was performed, considering several experimental workloads. Simulations indicate that First-Fit Decreasing algorithm (A4) outperforms other evaluated heuristics on average. Experimental results prove that an offline memetic algorithm improves the quality of the solutions with migrations of VMs at the expense of placement reconfigurations.