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A pattern fusion model for multi-step-ahead CPU load prediction

A pattern fusion model for multi-step-ahead CPU load prediction. Systems and Software Dingyu Yanga , Jian Caoa ,∗, Jiwen Fua , Jie Wangb , Jianmei Guoc 鍾舜璽. Introduction.

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A pattern fusion model for multi-step-ahead CPU load prediction

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  1. A pattern fusion model for multi-step-ahead CPU load prediction Systems and Software DingyuYanga, JianCaoa,∗, JiwenFua, JieWangb, JianmeiGuoc 鍾舜璽

  2. Introduction • Resource availability often changes from time to time and schedulers needs a global view of all resources in a distributed system. • Sometimes high CPU load means performance anomalies that may result in system collapse.

  3. WNN and PSF • Weighted Nearest Neighbors (WNN) algorithm • Pattern Sequence-based Forecasting (PSF) algorithm • Euclidean distance • not enough to match the similar patterns.

  4. WNN and PSF (cont.) • One-step-ahead prediction strategy • if the one-step-ahead value is not accurate, the predictions for the following points in time will become more and more inaccurate.

  5. A pattern fusion model for multi-step-ahead CPU load prediction

  6. Pattern extraction

  7. Pattern extraction (cont.)

  8. Pattern extraction (cont.)

  9. Pattern extraction (cont.) • Pattern filtering • Pattern merging

  10. Pattern matching

  11. Pattern matching (cont.) • Pattern similarity matching • Euclidean distance matching • Fluctuation pattern set matching

  12. Pattern weighting strategy • Average rule strategy

  13. Pattern weighting strategy (cont.) • Uniform decline strategy

  14. Pattern weighting strategy (cont.)

  15. Pattern weighting strategy (cont.)

  16. Prediction results function • Adaboost algorithm

  17. Prediction results function (cont.) • Prediction values from different values of various length patterns:

  18. Experiment • Experiment settings • Mean absolute error (MAE) • Mean relative error (MRE)

  19. Experiment (cont.)

  20. Experiment (cont.) • Pattern similarity measurement • The influences of filter parameter

  21. Experiment (cont.) • Different pattern weighting strategies • Multi-step-ahead predictions

  22. Experiment (cont.) • Comparison with iterative one-step-ahead prediction

  23. Experiment (cont.) • Comparisons with other prediction approaches • Run time

  24. Conclusion • The contribution of this paper is a fusion model for multi-step-ahead CPU load prediction. • We plan to consider longer steps and improve the prediction accuracy in future. • In addition, multivariate prediction, such as memory usage and disk usage predictions are also interesting topics.

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