MULTI-DIMENSIONAL BATCH SCHEDULING FRAMEWORK FOR HIGH-END SUPERCOMPUTERS

In the field of high performance computing (HPC), batch scheduling plays a critical role. They determine when and how to process the various jobs waiting for service. Conventional batch schedulers allocate user jobs solely based on their CPU footprints. However, for a given user job, it requires many different resources during its execution, such as power, network, I/O bandwidth, etc. Today’s job schedulers rarely take into account these resource requirements which sometimes turn out to be the Achilles’ heel of system-wide performance. In this research, we propose a multi-dimensional batch scheduling framework for high-end supercomputers. Our research aims to treat these common but often ignored resources (e.g., power, network, bandwidth) as schedulable resource and further transform each scheduling into a multi-objective optimization process. Our main contributions consist of a set of scheduling models and policies, aiming at addressing the issues in batch scheduling for large-scale production supercomputers. We evaluate our design by means of trace-based simulations using real workload and performance traces from production systems. Experimental results show our methods can effectively improve batch scheduling regarding user satisfaction, system performance and operating cost.Ph.D. in Computer Science, May 201