Workload generation for virtual machine placement in cloud computing environments

Abstract

Cloud computing datacenters provide millions of virtual machines (VMs) in actual cloud markets. Nowadays, efficient location of these VMs into available physical machines (PMs) represents a research challenge, considering the large number of existing formulations and optimization criteria. Several techniques have been studied for the Virtual Machine Placement (VMP) problem. However, each article performs experiments with different datasets, making difficult the comparison between different formulations and solution techniques. Considering the absence of a highly recognized and accepted benchmark to study the VMP problem, this work proposes and implements a Workload Generator to enable the generation of different instances of the VMP problem for cloud computing environments, based on different configurable parameters. Additionally, this work also provides a set of pre-generated instances of the VMP that facilitates the comparison of different solution techniques of the VMP problem for the most diverse dynamic environments identified in the state-of-the-art.