TetriSched
- 18 April 2016
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
Abstract
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.Keywords
This publication has 28 references indexed in Scilit:
- Market mechanisms for managing datacenters with heterogeneous microarchitecturesACM Transactions on Computer Systems, 2014
- QoS-Aware scheduling in heterogeneous datacenters with paragonACM Transactions on Computer Systems, 2013
- Apache Hadoop YARNPublished by Association for Computing Machinery (ACM) ,2013
- alschedPublished by Association for Computing Machinery (ACM) ,2012
- Interactive analytical processing in big data systemsProceedings of the VLDB Endowment, 2012
- OoziePublished by Association for Computing Machinery (ACM) ,2012
- JockeyPublished by Association for Computing Machinery (ACM) ,2012
- Modeling and synthesizing task placement constraints in Google compute clustersPublished by Association for Computing Machinery (ACM) ,2011
- Delay schedulingPublished by Association for Computing Machinery (ACM) ,2010
- MapReduceCommunications of the ACM, 2008