PDL Abstract

TetriSched: Global Rescheduling with Adaptive Plan-ahead in Dynamic Heterogeneous Clusters

ACM European Conference on Computer Systems, 2016 (EuroSys'16), 18th-21st April, 2016, London, UK.

Alexey Tumanov, Timothy Zhu, Jun Woo Park, Michael A. Kozuch*, Mor Harchol-Balter,
Gregory R. Ganger

Carnegie Mellon University
Pittsburgh, PA

*Intel Labs

TetriSched is a scheduler that works in tandem with a calendaring reservation system to continuously re-evaluate the immediate-term scheduling plan for all pending jobs (including those with reservations and best-effort jobs) on each scheduling cycle. TetriSched leverages information supplied by the reservation system about jobs’ deadlines and estimated runtimes to plan ahead in deciding whether to wait for a busy preferred resource type (e.g., machine with a GPU) or fall back to less preferred placement options. Plan-ahead affords significant flexibility in handling mis-estimates in job runtimes specified at reservation time. Integrated with the main reservation system in Hadoop YARN, TetriSched is experimentally shown to achieve significantly higher SLO attainment and cluster utilization than the best-configured YARN reservation and CapacityScheduler stack deployed on a real 256 node cluster.