Real-time gang schedulings with workload models for parallel computers

Abstract

Gang scheduling has been shown to be an effective job scheduling policy for parallel computers that combines elements of space sharing and time sharing. We propose new policies to enable gang scheduling to adapt to environments with real-time constraints. Our work, to our best knowledge, is the first work to attempt to address the real-time aspects of gang scheduling. Our system guided by a metric, called "task utilization workload", can schedule both real-time and non-real-time tasks at the same time. We report simulation results with a family of scheduling algorithms based on our proposed metric. Our scheme is designed to be a practical scheme to be used for large scale industrial and commercial parallel systems. Preliminary simulation results also show that our proposed policy is an effective scheme to perform real-time scheduling, while scheduling non-real-time jobs with fairness and good throughput.