Spread-n-share: improving application performance and cluster throughput with resource-aware job placement

Traditional batch job schedulers adopt the Compact-n-Exclusive (CE) strategy, packing processes of a parallel job into as few compute nodes as possible. While CE minimizes inter-node network communication, it often brings self-contention among tasks of a resource-intensive application. Recent studies have used virtual containers to balance CPU utilization and memory capacity across physical nodes, but the imbalance in cache and memory bandwidth usage is still under-investigated. In this work, we propose Spread-n-Share (SNS): a new batch scheduling strategy that automatically scales resource-bound applications out onto more nodes to alleviate their performance bottleneck, and co-locate jobs in a resource compatible manner. We implement Uberun, a prototype scheduler to validate SNS, considering shared-cache capacity and memory bandwidth as two types of performance-critical shared resources. Experimental results using 12 diverse cluster workloads show that SNS improves the overall system throughput by 19.8% on average over CE, while achieving an average individual job speedup of 1.8%.