New Metrics for Scheduling Jobs on a Cluster of Virtual Machines

As the virtualization of resources becomes popular, the scheduling problem of batch jobs on virtual machines requires new approaches. The dynamic and sharing aspects of virtual machines introduce unique challenges and complexity for the scheduling problems of batch jobs. In this paper, we propose a new set of metrics, called potential capacity (PC) and equilibrium capacity (EC), of resources that incorporate these dynamic, elastic, and sharing aspects of co-located virtual machines. We then show that we mesh this set of metrics smoothly into traditional scheduling algorithms. We evaluate the performance in using the metrics in a widely used greedy scheduling algorithm and show that the new scheduler improves job speedup for various configurations when compared to a similar algorithm using traditional physical machine metrics such as available CPU capacity.