Evaluation of gang scheduling performance and cost in a cloud computing system

Cloud Computing refers to the notion of outsourcing on-site available services, computational facilities, or data storage to an off-site, location-transparent centralized facility or Cloud. Gang Scheduling is an efficient job scheduling algorithm for time sharing, already applied in parallel and distributed systems. This paper studies the performance of a distributed Cloud Computing model, based on the Amazon Elastic Compute Cloud (EC2) architecture that implements a Gang Scheduling scheme. Our model utilizes the concept of Virtual Machines (or VMs) which act as the computational units of the system. Initially, the system includes no VMs, but depending on the computational needs of the jobs being serviced new VMs can be leased and later released dynamically. A simulation of the aforementioned model is used to study, analyze, and evaluate both the performance and the overall cost of two major gang scheduling algorithms. Results reveal that Gang Scheduling can be effectively applied in a Cloud Computing environment both performance-wise and cost-wise.