Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Investing the Effects of Overcommitting YARN resources

916 views

Published on

Investing the Effects of Overcommitting YARN resources

Published in: Technology
  • Be the first to comment

  • Be the first to like this

Investing the Effects of Overcommitting YARN resources

  1. 1. Investigating the Effects of Overcommitting YARN Resources Jason Lowe jlowe@yahoo-inc.com
  2. 2. Problem: Underutilized Cluster Resources
  3. 3. Optimize The Jobs! ● Internal Downsizer tool quantifies job waste ● Application framework limitations ● Optimally tuned container can still have opportunities Time ContainerUtilization Underutilized Resources
  4. 4. What about Static Overcommit? ● Configure YARN to use more memory than node provides ● Tried with some success ● Performs very poorly when node fully utilized
  5. 5. Overcommit Prototype Design Goals ● No changes to applications ● Minimize changes to YARN protocols ● Minimize changes to scheduler internals ● Overcommit on memory only ● Conservative growth ● Rapid correction
  6. 6. Overcommit Overview ResourceManager NodeManager Utilization report in heartbeat ■ Unaware of overcommit amount ■ Self-preservation preemption ■ Adjusts internal node size ■ Assigns containers based on new size Application Masters
  7. 7. NodeMemoryNodeUtilization ResourceManager Node Scaling Time Time No Overcommit Reduced Overcommit Full Overcommit Allocated Node Mem Total Node Mem Original Node Mem
  8. 8. ResourceManager Overcommit Tunables Parameter Description Value memory.max-factor Maximum amount a node will be overcommitted 1.5 memory.low-water-mark Maximum overcommit below this node utilization 0.6 memory.high-water-mark No overcommit above this node utilization 0.8 memory.increment-mb Maximum increment above node allocation 16384 increment-period-ms Delay between overcommit increments if node container state does not change 0 Parameters use yarn.resourcemanager.scheduler.overcommit. prefix
  9. 9. NodeManager Self-Preservation Preemption Node Utilization High Water Mark Low Water Mark ● Utilization above high mark triggers preemption ● Preempts enough to reach low mark utilization ● Does not preempt containers below original node size ● Containers preempted in group order ○ Tasks from preemptable queue ○ ApplicationMasters from preemptable queue ○ Tasks from non-preemptable queue ○ ApplicationMasters from non-preemptable queue ● Youngest containers preempted first within a group 0% 100%
  10. 10. NodeManager Overcommit Tunables Parameter Description Value memory.high-water-mark Preemption when above this utilization 0.95 memory.low-water-mark Target utilization after preemption 0.92 Parameters use yarn.nodemanager.resource-monitor.overcommit. prefix
  11. 11. Results
  12. 12. Results - Capacity_Gained vs Work_Lost
  13. 13. Lessons Learned ● Significant overcommit achievable on real workloads ● Far less preemption than expected ● Container reservations can drive overcommit growth ● Coordinated reducers can be a problem ● Cluster totals over time can be a bit confusing at first
  14. 14. Future Work ● YARN-5202 ● Only grows cluster as a whole not individual queues ● Nodes can overcommit while others are relatively idle ● CPU overcommit ● Predict growth based on past behavior ● Relinquish nodes during quiet periods ● Integration with YARN-1011
  15. 15. YARN-1011 ● Explicit GUARANTEED vs. OPPORTUNISTIC distinction ● Promotion of containers once resources are available ● SLA guarantees along with best-effort load
  16. 16. Acknowledgements ● Nathan Roberts for co-developing overcommit POC ● Inigo Goiri for nodemanager utilization collection and reporting ● Giovanni Matteo Fumarola for nodemanager AM container detection ● YARN-1011 contributors for helping to shape the long-term solution
  17. 17. Questions? Jason Lowe jlowe@yahoo-inc.com

×