Simulation Based Job Scheduling Optimization for Batch Workloads

We present a simulation based approach for scheduling jobs that are part of a batch workflow. Our objective is to minimize the makespan, defined as completion time of the last job to leave the system in a batch workflow with dependencies. The existing job schedulers make scheduling decisions based on available cores, memory size, priority or execution time of jobs. This does not guarantee minimum makespan since contention for resources among concurrently running jobs are ignored. In our approach, prior to scheduling batch jobs on physical servers, we simulate the execution of jobs using a discrete event simulator. The simulator considers available cores and available memory bandwidth on distributed systems to accurately simulate the execution of jobs using resource contention models in a concurrent run. We also propose simulation based job scheduling algorithms that use underlying contention models and minimize the makespan by optimally mapping jobs onto the available nodes. Our approach ensures that job dependencies are adhered to during the simulation. We assess the efficacy of our job scheduling algorithms and contention models by performing experiments on a real cluster. Our experimental results show that simulation based approach improves the makespan by 15% to 35% depending on the nature of workload.