A Game Based Consolidation Method of Virtual Machines in Cloud Data Centers With Energy and Load Constraints

Abstract

To reduce energy consumption and balance the resource load of physical machines (PMs) in cloud data centers, we present a game-based consolidation method of virtual machines (VMs) with energy and load (applied load) constraints. First, we test every measured value of the resource load using a t-test to filter outliers. Based on these values, the future resource load is forecast using gray theory. Second, all online PMs are grouped by the number of VMs on them and their future load values. Based on the groupings, a pre-processing algorithm for selecting destination PMs is proposed to determine a set of destination PMs for a VM awaiting migration. Finally, we select the final destination PM for the VM using game-based methods aimed at optimizing overall energy consumption. The experimental results show that our method can reduce energy consumption as well as balance loads without unnecessarily increasing the number of VM migrations.