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Load Analysis and Prediction for Responsive Interactive Applications

Load Analysis and Prediction for Responsive Interactive Applications. Peter A. Dinda David R. O’Hallaron Carnegie Mellon University. Overview. Load Analysis. Time Series Modelling. Measurement. History-based Load Prediction. Communication. Computation. Execution Time Predicition.

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Load Analysis and Prediction for Responsive Interactive Applications

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  1. Load Analysis and Predictionfor Responsive Interactive Applications Peter A. Dinda David R. O’Hallaron Carnegie Mellon University

  2. Overview Load Analysis Time Series Modelling Measurement History-based Load Prediction Communication Computation Execution Time Predicition Remote Execution Best Effort Real-time Responsive Interactive Applications (eg, BBN OpenMap)

  3. OpenMap (BBN) “Move North” Integrator New map data Choice of Host Bounded Response Time Replicated Specialists Terrain Terrain Terrain

  4. Context Advanced Mobility Platform Logistics Anchor Desk METOC Anchor Desk Other Applications JTF Planner TRACE2ES Applications ... Frameworks OpenMap (BBN) QuO (BBN) Adaptation Load Prediction (CMU) Prediction Remos (CMU) Measurement Distributed system Distributed system

  5. Appropriate Time Series Models Load Trace Collection Statistical Analysis Fitted Models Evaluation/ Comparison On-line Predictors Load Analysis and Prediction • Goal:accurate short term predictions • Few seconds for non-stale data • Evaluation/comparison issues • Load generation vs. Load prediction • Have to discover which properties are important • Performance measure • Mean squared prediction error • Lack of lower bound to compare against • Simple, reasonable algorithm for comparison

  6. Load Trace Analysis • Digital Unix one minute load average • Four classes of hosts (38 machines) • 1 Hz sample rate, >one week traces, two sets at different times of the year • Analysis results to appear in LCR98 • Load is self-similar • Load exhibits epochal behavior

  7. Self-similarity Statistics

  8. Why is Self-Similarity Important? • Complex structure • Not completely random, nor independent • Short range dependence • Excellent for history-based prediction • Long range dependence • Possibly a problem • Modeling Implications • Suggests models • ARFIMA, FGN, TAR

  9. Load Exhibits Epochal Behavior

  10. Epoch Length Statistics

  11. Why is Epochal Behavior Important? • Complex structure • Non-stationary • Modeling Implications • Suggests models • ARIMA, ARFIMA, etc. • Non-parametric spectral methods • Suggests problem decomposition

  12. Time Series Prediction of Load Nonlinear Linear Markov TAR Parametric Non-parametric Stationary Non-stationary ARMA, AR, MA Self-similar Non-self-similar “Best Mean” ARFIMA, FGN ARIMA

  13. t+1 Predictions

  14. t+5 Prediction

  15. Conclusions • Load has structureto exploit for prediction • Structure is complex (self-similarity, epochs) • Simple time series models are promising • Benefits of more sophisticated models are unclear • Current research questions • What are the benefits of more sophisticated models? • How to characterize prediction error to user? • Is there a measure of inherent predictability? • How to incorporate load prediction into systems?

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