Openstack scheduler evaluation using design of experiment approach

Abstract

Cloud computing is a computing model which is essentially characterized by an on-demand and dynamic provisioning of computing resources. In this model, a cloud is a large-scale distributed system which leverages internet and virtualization technologies to provide computing resources as a service. Efficient, flexible and dynamic resource management is among the most challenging research issues in this domain. In this context, we present a study focusing on the dynamic behavior of the scheduling functionality of an Infrastructure-as-a-Service (IaaS) cloud, namely OpenStack Scheduler. We aim, through this study at identifying the limitations of this scheduler and ultimately enabling its extension using enhanced metrics. Towards this end, we present a Design of Experiment (DOE) based approach for the evaluation of the OpenStack Scheduler behavior. In particular, we use the screening type of experiment to identify the factors with significant effects on the responses. In our context, these factors are the amount of memory and the number of CPU cores assigned to virtual machine (VM) and the amount of memory and the number of cores on physical nodes. More specifically, we present a two-level fractional factorial balanced with the resolution IV and four center points experimental design with no replication.