JobPacker: Job Scheduling for Data-Parallel Frameworks with Hybrid Electrical/Optical Datacenter Networks

In spite of many advantages of hybrid electrical/optical datacenter networks (Hybrid-DCN), current job schedulers for data-parallel frameworks are not suitable for Hybrid-DCN, since the schedulers do not aggregate data traffic to facilitate using optical circuit switch (OCS). In this paper, we propose JobPacker, a job scheduler for data-parallel frameworks in Hybrid-DCN that aims to take full advantage of OCS to improve job performance. JobPacker aggregates the data transfers of a job in order to use OCS to improve data transfer efficiency. It first explores the tradeoff between parallelism and traffic aggregation for each shuffle-heavy recurring job, and then generates an offline schedule including which racks to run each job and the sequence to run the recurring jobs in each rack that yields the best performance. It has a new sorting method to prioritize recurring jobs in offline-scheduling to prevent high resource contention while fully utilizing cluster resources. In real-time scheduler, JobPacker uses the offline schedule to guide the data placement and schedule recurring jobs, and schedules non-recurring jobs to the idle resources not assigned to recurring jobs. Trace-driven simulation and GENI-based emulation show that JobPacker reduces the makespan up to 49% and the median completion time up to 43%, compared to the state-of-the-art schedulers in Hybrid-DCN.