Virtual machine placement for elastic infrastructures in overbooked cloud computing datacenters under uncertainty

Abstract

Infrastructure as a Service (IaaS) providers must support requests for virtual resources in highly dynamic cloud computing environments. Due to the randomness of customer requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. This work presents a novel two-phase optimization scheme for the resolution of VMP problems for cloud computing under uncertainty of several relevant parameters, combining advantages of online and offline formulations in dynamic environments considering service elasticity and overbooking of physical resources. In this context, a formulation of a VMP problem is presented, considering the optimization of the following four objective functions: (i) power consumption, (ii) economical revenue, (iii) resource utilization and (iv) reconfiguration time. The proposed two-phase optimization scheme includes novel methods to decide when to trigger a placement reconfiguration through migration of virtual machines (VMs) between physical machines (PMs) and what to do with VMs requested during the placement recalculation time. An experimental evaluation against state-of-the-art alternative approaches for VMP problems was performed considering 400 scenarios. Experimental results indicate that the proposed methods outperform other evaluated alternatives, improving the quality of solutions in a scenario-based uncertainty model considering the following evaluation criteria: (i) average, (ii) maximum and (iii) minimum objective function costs.