Dynamic Fractional Resource Scheduling -- 2010, ARCS

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Dynamic Fractional Resource Scheduling -- 2010, ARCS

  1. 1. Introduction Off-Line Problem On-Line Problem Summary Appendix Dynamic Fractional Resource Scheduling for Cluster Platforms Mark Stillwell Department of Information and Computer Sciences University of Hawai’i at M¯anoa Achievement Rewards for College Scientists 2010 Scholarship Awards Program Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  2. 2. Introduction Off-Line Problem On-Line Problem Summary Appendix Clusters Definition A cluster is a group of independent computers, or nodes, working together closely, usually connected by a high-speed network. Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  3. 3. Introduction Off-Line Problem On-Line Problem Summary Appendix Jobs The system can accept user requests to run jobs or the administrator can instantiate jobs directly Running jobs are made up of nearly identical tasks The number of tasks is specified by user/administrator Tasks can block while communicating with each other The assignment of resources to tasks is called scheduling Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  4. 4. Introduction Off-Line Problem On-Line Problem Summary Appendix Current Approaches Service Hosting (Off-Line scheduling) traditionally, dedicated machines current interest in server consolidation few good theoretical models heavy “engineering” bias High-Performance Computing (On-Line Scheduling) Usually First-Come-First-Served (FCFS) with backfilling Backfilling needs (unreliable) compute time estimates Unbounded wait times Inefficient use of nodes/resources Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  5. 5. Introduction Off-Line Problem On-Line Problem Summary Appendix Our Proposal Use virtual machine technology. Multiple tasks on one node Performance isolation Sharing of fractional resources Define a run-time computable metric that captures notions of performance and fairness. Design heuristics that allocate resources to jobs while explicitly trying to achieve high ratings by our metric. Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  6. 6. Introduction Off-Line Problem On-Line Problem Summary Appendix Requirements, Needs, and Yield Tasks have memory requirements and CPU needs All tasks of a job have the same requirements and needs For a task to be placed on a node there must be memory available at least equal to its requirements A task can be allocated less CPU than its need, and the ratio of the allocation to the need is the yield All tasks of a job must have the same yield, so we can also speak of the yield of a job The yield of a job gives its performance relative to if it were run on a dedicated system Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  7. 7. Introduction Off-Line Problem On-Line Problem Summary Appendix Off-Line Problem Assumptions Steady-state execution with infinite jobs Models an ideal service hosting environment Makes the problem more tractable [Marchal et al., 2006] Good when job duration longer than schedule time Jobs are serial (single task) Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  8. 8. Introduction Off-Line Problem On-Line Problem Summary Appendix Objective Function The performance of a job is correlated to its yield Maximizing the average or sum of the yields may be unfair to some jobs Instead, we seek to maximize the minimum yield Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  9. 9. Introduction Off-Line Problem On-Line Problem Summary Appendix Task Placement Heuristics Greedy Task Placement – Incremental, puts each task on the node with the lowest computational load on which it can fit without violating memory constraints Randomized Rounding Task Placement – Based on relaxing an MILP to an LP and rounding probabilistically MCB Task Placement – Global, iteratively applies multi-capacity (vector) bin-packing heuristics during a binary search for the maximized minimum yield Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  10. 10. Introduction Off-Line Problem On-Line Problem Summary Appendix Large Problem Set: Minimum Yield vs. Free Memory 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.3 0.35 0.4 0.45 0.5 0.55 Slack MinimumYield bound MCB8 GR GB SG SGB RRND RRNZ Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  11. 11. Introduction Off-Line Problem On-Line Problem Summary Appendix Conclusions The problem is tractable Multi-capacity bin packing algorithms perform well Greedy algorithms perform okay, and are very fast Randomized rounding approaches are not very promising Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  12. 12. Introduction Off-Line Problem On-Line Problem Summary Appendix On-Line Problem Assumptions Job arrival/completion times are not known in advance Jobs are temporary The user wants a final result Quick turnaround relative to runtime is desired Jobs are not interactive So can wait until resources are available to start We avoid the use of runtime estimates Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  13. 13. Introduction Off-Line Problem On-Line Problem Summary Appendix Stretch Our goal: minimize maximum stretch (aka slowdown) Stretch: the time a job spends in the system divided by the time that would be spent in a dedicated system [Bender et al., 1998] Popular to quantify schedule quality post-mortem Not generally used to make scheduling decisions Runtime computation requires (unreliable) user estimates. Minimizing average stretch prone to starvation Minimizing maximum stretch captures notions of both performance and fairness. Our approach: try to maximize minimum yield Similar, but not the same, as minimizing maximum stretch Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  14. 14. Introduction Off-Line Problem On-Line Problem Summary Appendix Max Stretch Degradation vs. Load, no migration cost 1 10 100 1000 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 StretchDegradationFactor Load EASY FCFS GREEDY GREEDYP GREEDYPM DynMCB8 MCB8P 600 MCB8PG 600 Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  15. 15. Introduction Off-Line Problem On-Line Problem Summary Appendix Conclusions DFRS approaches can significantly outperform traditional approaches Aggressive repacking can lead to much better resource allocations But also to heavy migration costs Greedy migration is not that useful Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  16. 16. Introduction Off-Line Problem On-Line Problem Summary Appendix Summary We have proposed a novel approach to job scheduling on clusters, Dynamic Fractional Resource Scheduling, that makes use of modern virtual machine technology and seeks to optimize a runtime-computable, user-centric measure of performance called the minimum yield Multi-capacity bin packing heuristics can be used to find good solutions Our approach avoids the use of unreliable runtime estimates This approach has the potential to lead to order-of-magnitude improvements in performance over current technology Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  17. 17. Introduction Off-Line Problem On-Line Problem Summary Appendix References I Bender, M. A., Chakrabarti, S., and Muthukrishnan, S. (1998). Flow and Stretch Metrics for Scheduling Continuous Job Streams. In Proc. of the 9th ACM-SIAM Symp. On Discrete Algorithms, pages 270–279. Marchal, L., Yang, Y., Casanova, H., and Robert, Y. (2006). Steady-state scheduling of multiple divisible load applications on wide-area distributed computing platforms. Intl. J. of High Performance Computing Applications, 20(3):365–381. Mark Stillwell UH M¯anoa ICS DFRS for Clusters
  18. 18. Introduction Off-Line Problem On-Line Problem Summary Appendix References II Stillwell, M., Shanzenbach, D., Vivien, F., and Casanova, H. (2009). Resource Allocation using Virtual Clusters. In CCGrid, pages 260–267. IEEE. Stillwell, M., Vivien, F., and Casanova, H. (2010). Dynamic fractional resource scheduling for HPC workloads. In IPDPS. to appear. Mark Stillwell UH M¯anoa ICS DFRS for Clusters

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