A Stable Matching Algorithm for VM Migration to Improve Energy Consumption and QOS in Cloud Infrastructures

Abstract

Cloud Computing is one of the fast spreading technologies for providing utility-based IT services to its users. Large-scale virtualized datacenters are established in order to provide these services. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, datacenters hosting Cloud applications consume huge amounts of electrical energy, contributing to high operational cost for the service providers as well as for the service users. Energy consumption can be reduced by live migration of virtual machines (VM) as required and by switching off idle physical machines (PM). Therefore, we propose an approach that finds a stable matching fair to both VMs and PMs, to improve the energy consumption without affecting the quality of service, instead of favoring either side because of a deferred acceptance procedure. The approach presumes two dynamics thresholds, and prepares those virtual machines on the physical machines that the load is over one of the two presumed values to be migrated. Before migrating all those VMs, we use the Coase theorem to determine the number of VMs to migrate for optimal costs. Our approach aims to improve energy consumption of the datacenters, while delivering an expected Quality of Service.