New scoring formula to rank hypervisors’ performance complementing with statistical analysis using DOE

Abstract

Hypervisors enable cloud computing model to provide scalable infrastructures and on-demand access to computing resources as they support multiple operating systems to run on one physical server concurrently. This mechanism enhances utilization of physical server thus reduces server count in the data center. Hypervisors also drive the benefits of reduced IT infrastructure setup and maintenance costs along with power savings. It is interesting to know different hypervisors’ performance for the consolidated application workloads. Three hypervisors ESXi, XenServer, and KVM are carefully chosen to represent three categories full virtualized, para-virtualized, and hybrid virtualized respectively for the experiment. We have created a private cloud using CloudStack. Hypervisors are deployed as hosts in the private cloud in the respective clusters. Each hypervisor is deployed with three virtual machines. Three applications web server, application server, and database servers are installed on three virtual machines. Experiments are designed using Design of Experiments (DOE) methodology. With concurrently running virtual machines, each hypervisor is stressed with the consolidated real-time workloads (web load, application load, and OLTP) and important system information is gathered using SIGAR framework. The paper proposes a new scoring formula for hypervisors’ performance in the private cloud for consolidated application workloads and the accuracy of the results are complemented with sound statistical analysis using DOE.