Toward balanced and sustainable job scheduling for production supercomputers

Job scheduling on production supercomputers is complicated by diverse demands of system administrators and amorphous characteristics of workloads. Specifically, various scheduling goals such as queuing efficiency and system utilization are usually conflicting and thus need to be balanced. Also, changing workload characteristics often impact the effectiveness of the deployed scheduling policies. Thus it is challenging to design a versatile scheduling policy that is effective in all circumstances. In this paper, we propose a novel job scheduling strategy to balance diverse scheduling goals and mitigate the impact of workload characteristics. First, we introduce metric-aware scheduling, which enables the scheduler to balance competing scheduling goals represented by different metrics such as job waiting time, fairness, and system utilization. Second, we design a scheme to dynamically adjust scheduling policies based on feedback information of monitored metrics at runtime. We evaluate our design using real workloads from supercomputer centers. The results demonstrate that our scheduling mechanism can significantly improve system performance in a balanced, sustainable fashion.