Bistro: scheduling data-parallel jobs against live production systems
Andrey Goder, Alexey Spiridonov, Yin Wang · USENIX Annual Technical Conference · 2015
Data-intensive batch jobs increasingly compete for resources with customer-facing online workloads in modern data centers. Today, the two classes of workloads run on separate infrastructures using different resource managers that pursue different objectives. Batch processing systems strive for coarse-grained throughput whereas online systems must keep the latency of fine-grained end-user requests low. Better resource management would allow both batch and online workloads to share infrastructure, reducing hardware and eliminating the inefficient and error-prone chore of creating and maintaining copies of data. This paper describes Facebook's Bistro, a scheduler that runs data-intensive batch jobs on live, customer-facing production systems without degrading the end-user experience. Bistro employs a novel hierarchical model of data and computational resources. The model enables Bistro to schedule workloads efficiently and adapt rapidly to changing configurations. At Facebook, Bistro is replacing Hadoop and custom-built batch schedulers, allowing batch jobs to share infrastructure with online workloads without harming the performance of either.