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A Prediction-based Real-time Scheduling Advisor

RTSA offers predictive performance advice for adaptive interactive applications in shared computing environments.

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A Prediction-based Real-time Scheduling Advisor

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  1. A Prediction-basedReal-time Scheduling Advisor Peter A. Dinda Prescience Lab Department of Computer Science Northwestern University http://www.cs.northwestern.edu/~pdinda

  2. RTSA “I have a 5 second job. I want it to finish in under 10 seconds with at least 95% probability. Here is a list of hosts where I can run it. Which one should I use?” Real-time Scheduling Advisor “Use host 3. It’ll finish there in 7 to 9 secs” “There is no host where that is possible. The fastest is host 5, where it’ll finish in 12 to 15 seconds.”

  3. Core Results • RTSA based on predictive signal processing • Layered system architecture for scalable performance prediction • Targets commodity shared, unreserved distributed environments • All at user level • Randomized trace-based evaluation giving evidence of its effectiveness • Limitations • Compute-bound tasks • Evaluation on Digital Unix platform Publicly available as part of RPS system

  4. Outline • Motivation: interactive applications • Interface • Implementation • Performance evaluation • Conclusions and future work

  5. Interactive Applications on Shared, Unreserved Distributed Computing Environments • Examples: visualization, games, vr • Responsiveness requirements => soft deadlines • No resource reservation or admission control • Constant competition from other users • Changing resource availability => adaptation • Adaptation is via server selection • Other mechanisms possible

  6. Interactive Applications and the RTSA • RTSA controls adaptation mechanisms • Operates on behalf of single application • Multiple RTSAs may be running independently • Current limitation: Compute-bound tasks

  7. Interface - Request int RTSAAdviseTask(RTSARequest &req, RTSAResponse &resp); struct RTSARequest { double tnom; double sf; double conf; Host hosts[]; }; Size of task in CPU-seconds Maximum slack allowed Minimum probability allowed List of hosts to choose from deadline = now + tnom(1+sf) “I have a 5 second job. I want it to finish in under 10 seconds with at least 95% probability. Here is a list of hosts where I can run it. Which one should I use?”

  8. Interface - Response int RTSAAdviseTask(RTSARequest &req, RTSAResponse &resp); struct RTSAResponse { double tnom; double sf; double conf; Host host; RunningTimePredictionResponse runningtime; }; Size of task in CPU-seconds Maximum slack allowed Minimum probability allowed Host to use Predicted running timeof task on that host “Use host 3. It’ll finish there in 7 to 9 secs”

  9. RunningTimePredictionResponse struct RunningTimePredictionResponse { Host host; double tnom; double conf; double texp; double tlb; double tub; }; Host to use Size of task in CPU-seconds Confidence level Point estimate of running time Confidence interval of running time “The most likely running time is 7.5 seconds. There is a 95% chance that the actual running time will be in the range 7 to 9 seconds.”

  10. Implementation

  11. Underlying Components • Host load measurement • Digital Unix 5 second load average, 1 Hz • [LCR98, SciProg99] • Host load prediction • Periodic linear time series analysis (continuously monitored AR(16) predictors) • <1% of CPU • [HPDC99, Cluster00] • Running time advisor (RTA) • Task size + host load predictions => confidence interval for running time of task • [SIGMETRICS01,HPDC01,Cluster02]

  12. RTSA Implementation Simplified Predicted Running Time RTA predicts running time on each host texp ? Task tnom

  13. RTSA Implementation Simplified RTSA picks randomly from among the hosts where the deadline can be met If there is no such host, RTSA returns the host with the lowest running time RTSA also returns the estimate of the running time Predicted Running Time deadline ? Task tnom deadline=(1+sf)tnom

  14. Prediction Error • Predictions are not perfect • Some machines harder to predict than others • Need more than a point estimate (texp) • Predictors can estimate their quality • Covariance matrix for prediction errors • Estimate of predictor error also continually monitored for accuracy • Confidence interval captures this • Deadline probability serves as confidence level

  15. RTSA Implementation tub RTSA picks randomly from among the hosts where the deadline can be meteven given the maximum running time captured in the confidence interval If there is no such host, RTSA returns the host with the lowest running time RTSA also returns the estimate of the running time Predicted Running Time deadline tlb ? Task tnom deadline=(1+sf)tnom conf=95%

  16. Experimental Setup • Environment • Alphastation 255s, Digital Unix 4.0 • Private network • Separate machine for submitting tasks • Prediction system on each host • BG workload: host load trace playback [lcr00] • Traces from PSC Alpha cluster, wide range of CMU machines • Reconstruct any combination of these machines (scenario) • Testcase: submit synthetic task to system, run on host that RTSA selects, measure result

  17. Scenarios

  18. The Metrics • Fraction of deadlines met • Probability of meeting deadline • Fraction of deadlines met when predicted • Probability of meeting deadline if RTSA claims it is possible • Number of possible hosts • Degree of randomness in RTSA’s decision • High randomness means different RTSAs are unlikely to conflict

  19. Testcases • Synthetic compute-bound tasks • Size: 0.1 to 10 seconds, uniform • Interarrival: 5 to 15 seconds, uniform • sf: 0 to 2, uniform • conf: 0.95 in all cases 8,000 to 16,000 testcases for each scenario How do metrics vary with scenario, size, sf?

  20. The RTSA Implementations • AR(16) • RTSA as described here • Instantiated with the AR(16) load predictor • MEASURE • Send task to host with lowest load • Does not return predicted running time • High probability of conflicts • RANDOM • Send task to a random host • Does not return predicted running time • Low probability of conflicts

  21. Fraction of Deadlines Met – 4LS Performance gain from prediction

  22. Fraction of Deadlines Met – 4LS Performance gain from prediction

  23. Fraction of Deadlines Met – 4LS Highest performance gain from prediction near “critical slack”

  24. Fraction of Deadlines Met When Predicted – 4LS Only predictive strategy can indicate whether meeting deadline is possible

  25. Fraction of Deadlines Met When Predicted – 4LS Only predictive strategy can indicate whether meeting deadline is possible

  26. Fraction of Deadlines Met When Predicted – 4LS Operating near critical slack is most challenging

  27. Number of Possible Hosts – 4LS Predictive strategy introduces “appropriate randomness”

  28. Number of Possible Hosts – 4LS Predictive strategy introduces “appropriate randomness”

  29. Number of Possible Hosts – 4LS Operation near “critical slack” is most challenging

  30. Conclusions and Future Work • Introduced RTSA concept • Described prediction-based implementation • Demonstrated feasibility • Evaluated performance • Current and future work • Incorporate communication, memory, disk • Improved predictive models

  31. For MoreInformation • Peter Dinda • http://www.cs.northwestern.edu/~pdinda • RPS • http://www.cs.northwestern.edu/~RPS • Prescience Lab • http://www.cs.northwestern.edu/~plab

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