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

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

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

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

5. 5 Statistical Analysis Appropriate Time Series Models Fitted Models Evaluation/ Comparison On-line Predictors Load Trace Collection 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. 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. 7 Self-similarity Statistics

8. 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. 9 Load Exhibits Epochal Behavior

10. 10 Epoch Length Statistics

11. 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. 12 Time Series Prediction of Load Linear Nonlinear StationaryNon-stationary ARMA, AR, MA ARIMAARFIMA, FGN TARMarkov Self-similarNon-self-similar “Best Mean” Non-parametricParametric

13. 13 t+1 Predictions

14. 14 t+5 Prediction

15. 15 Conclusions Load has structure to 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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