Managing Risk of Inaccurate Runtime Estimates for Deadline Constrained Job Admission Control in Clusters Chee Shin Yeo and Rajkumar Buyya Grid Computing.

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Presentation transcript:

Managing Risk of Inaccurate Runtime Estimates for Deadline Constrained Job Admission Control in Clusters Chee Shin Yeo and Rajkumar Buyya Grid Computing and Distributed Systems (GRIDS) Lab. Dept. of Computer Science and Software Engineering The University of Melbourne, Australia

2 Problem/Motivation: Computing as a Service User-specific Service Level Agreement (SLA) Deadline QoS Deadline constrained job admission control in a cluster Prevents workload overload, service degradation Dependent on accurate runtime estimates Focus: Managing inaccurate runtime estimates

3 Related Work Cluster Resource Management System (RMS) Condor, LoadLeveler, LSF, OpenPBS, Sun Grid Engine Job admission control [Irwin04][Popovici05]: Utility [Islam04]: Soft Deadline Managing risk in computing jobs [Kleban04]: Job delay [Irwin04][Popovici05]: Penalty for job delay Job Scheduling with inaccurate runtime estimates [Mu ’ alem01][Sabin04][Tsafrir05]

4 Deadline Constrained Job Admission Control in a Cluster Cluster RMS Single interface for job submission Non-preemptive job scheduling Job submission No change in SLA after acceptance User-defined parameters Deadline QoS (Hard) Runtime estimate Number of processors

5 Libra: Deadline Constrained Job Admission Control in a Cluster Deadline-based Proportional Processor Share of a job i on node j (time-shared) [Sherwani04] Total share for n jobs on a node j Suitable node if deadline of all jobs (with new job) met BEST FIT strategy (least available processor time after accepting new job)

6 LibraRisk: Modeling Risk of Deadline Delay Delay of job i Deadline delay of job i [Kleban04] Mean deadline delay of n jobs on node j Risk of deadline delay of n jobs on node j

7 LibraRisk: Managing Risk of Deadline Delay Libra: Deadline-based Proportional Processor Share Different Admission Control Determine delay of all jobs (previously accepted jobs and new job) on each node if new job accepted Compute risk of deadline delay for each node Suitable node if zero risk Accept new job if sufficient number of suitable nodes as required by new job

8 Performance Evaluation: Simulation GridSim toolkit: Simulated scheduling in a cluster computing environment ( Feitelson’s Parallel Workload Archive ( Last 3000 jobs in SDSC SP2 trace Average inter arrival time: 2131 s (35.52 mins) Average run time: 8880 s (2.47 hrs) Average number of requested processors: 17 SDSC SP2 Number of computation nodes: 128

9 Experimental Methodology: Performance Evaluation Modeling deadline QoS [Irwin04] High urgency jobs (Default is 20%) Low deadline/runtime (Default mean is 4) Values normally distributed in each deadline/runtime Randomly distributed in arrival sequence Deadline high:low ratio (Default is 4) Ratio of means for deadline/runtime of low and high urgency jobs

10 Experimental Methodology: Performance Evaluation Earliest Deadline First (EDF) Space-shared Reselect a new job with an earlier deadline that arrives later Reject job prior to execution, not submission Libra Time-shared (Deadline-based proportional processor share) BEST FIT strategy (least available processor time after accepting new job)

11 Experimental Methodology: Performance Evaluation Arrival delay factor (Default is 1 – from trace) Model cluster workload thru job inter arrival time Inaccuracy of runtime estimates 0% - accurate runtime estimate (runtime) 100% - actual runtime estimate from trace Evaluation metrics % of jobs with deadlines fulfilled Average slowdown (jobs with deadlines fulfilled)

12 Impact of Varying Workload Less jobs fulfilled for actual runtime estimate from trace More jobs fulfilled with higher arrival delay LibraRisk: More jobs fulfilled (higher arrival delay) Jobs with Deadlines Fulfilled (%)

13 Impact of Varying Workload Lower slowdown for actual runtime estimate from trace Lower slowdown with higher arrival delay LibraRisk: Lower slowdown than Libra Average Slowdown

14 Impact of Varying Deadline High:Low Ratio More jobs fulfilled with higher deadline ratio LibraRisk: More jobs fulfilled (lower deadline ratio) Jobs with Deadlines Fulfilled (%)

15 Impact of Varying Deadline High:Low Ratio Higher slowdown with higher deadline ratio LibraRisk: Lower slowdown than Libra Average Slowdown

16 Impact of Varying High Urgency Jobs Less jobs fulfilled with more high urgency jobs LibraRisk: More jobs fulfilled (more high urgency jobs) Jobs with Deadlines Fulfilled (%)

17 Impact of Varying High Urgency Jobs Lower slowdown with more high urgency jobs LibraRisk: Lower slowdown than Libra Average Slowdown

18 Impact of Varying Inaccurate Runtime Estimates Less jobs fulfilled with higher inaccuracy of estimates LibraRisk: More jobs fulfilled (higher inaccuracy of estimates) Jobs with Deadlines Fulfilled (%)

19 Impact of Varying Inaccurate Runtime Estimates Lower slowdown with higher inaccuracy of estimates LibraRisk: Lower slowdown than Libra Average Slowdown

20 Conclusion Actual runtime estimate from trace Inaccurate and often over estimated LibraRisk Manage risk of deadline delay More jobs with deadlines fulfilled than EDF and Libra Lower cluster workload (higher arrival delay) More urgent jobs (shorter deadline) Less accurate runtime estimates Lower slowdown than Libra Future Work Backfilling

End of Presentation Questions ?