University of Calgary
Large-scale data analytics frameworks are shifting towards shorter task durations and larger degrees of parallelism to provide low latency. However, scheduling highly parallel jobs that complete in hundreds of milliseconds poses a major challenge for cluster schedulers, which will need to place millions of tasks per second on appropriate nodes while offering millisecond-level latency and high availability. The authors demonstrate that a decentralized, randomized sampling approach provides near-optimal performance while avoiding the throughput and availability limitations of a centralized design. They implement and deploy their scheduler, Sparrow, on a real cluster and demonstrate that Sparrow performs within 14%of an ideal scheduler.