Performance and cost evaluation of Gang Scheduling in a Cloud Computing system with job migrations and starvation handling

Abstract

Cloud Computing is an emerging technology in the area of parallel and distributed computing. Clouds consist of a collection of virtualized resources, which include both computational and storage facilities that can be provisioned on demand, depending on the users' needs. Gang Scheduling is an efficient technique for scheduling parallel jobs, already applied in the areas of Grid and Cluster computing. This paper studies the application of Gang Scheduling on a Cloud Computing model, based on the architecture of the Amazon Elastic Compute Cloud (EC2). The study takes into consideration both performance and cost while integrating mechanisms for job migration and handling of job starvation. The number of Virtual Machines (VMs) available at any moment is dynamic and scales according to the demands of the jobs being serviced. The aforementioned model is studied through simulation in order to analyze the performance and overall cost of Gang Scheduling with migrations and starvation handling. Results highlight that this scheduling strategy can be effectively deployed on Clouds, and that cloud platforms can be viable for HPC or high performance enterprise applications.