Abstract
Operational Data Analysis (ODA) automatically 1) monitors the performance of a computer through time, 2) stores such information in a data repository, 3) applies data-mining techniques, and 4) generates results. We describe a system implementing the four steps in ODA, focusing our attention on the data-mining step where our goal is to predict the value of a performance parameter (e.g., response time, cpu utilization, memory utilization) in the future. Our approach to the prediction problem extracts patterns from a database containing information from thousands of historical records and across computers. We show empirically how a multivariate linear regression model applied on all available records outperforms 1) a linear univariate model per machine, 2) a linear multivariate model per machine, and 3) a decision tree for regression across all machines. We conclude that global patterns relating characteristics across different computer models exist and can be extracted to improve the accuracy in predicting future performance behavior.
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Apte Chid and Hong Se June: Predicting Equity Returns from Securities Data. In Advances in Knowledge Discovery and Data Mining. Ed. Fayyad, U. M. and Pratetsky-Shapiro and Smyth, P. and Uthurusamy, R. AAAI Press, (1996) 541–560.
R. Wolski: Forecasting network performance to support dynamic scheduling using the network weather service. In Proceedings of the High Performance Distributed Computing Conference (1997).
K. Claffy and G. Polyzos: Tracking Long-term Growth of the NSFNET. In Communications of the ACM (1994).
Yeh T-Y. and Patt, Y.: Alternative implementation of Two-Level Adaptive Branch Prediction. In Proceedings of the 19th International Symposium on Computer Architecture, Gold Coast. Australia (1992) 124–134.
Brad, Calder and Dirk, Grunwald and Joel, Emer: A system level perspective on branch architecture performance. In Proceedings of the 28th Annual IEEE/ACM International Symposium onMicroarchitecture. Ann Arbor, MI (1995) 199–206.
Zhichen, Xu and Xiaodong, Zhang and Lin, Sun: Semi-Empirical Multiprocessor Performance Predictions. TR-96-05-01, University of Texas, San Antonio, High Performance Comp. and Software Lab (1996).
Mark E. Crovella and Thomas J. LeBlanc: Parallel performance prediction using lost cycles analysis. In Supercomputing 94 (1994).
C-H. Hsu and U. Kremer: A framework for qualitative performance prediction. In Department of Computer Science, Rutgers University. Technical Report LCSR-TR98-363 (1998).
Brad Calder and Dirk Grunwald: Next cache line and set prediction. In ACM (1995) 287–296.
N. P. Jouppi and P. Ranganathan: The relative importance of memory latency, bandwidth, and branch limits to performance. In The Workshop on Mixing Logic and DRAM: Chips that Compute and Remember (1997).
Weiss Sholom and Indurkhya Nitin: Predictive Data Mining. Morgan Kaufmann Publishers (1998).
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© 2000 Springer-Verlag Berlin Heidelberg
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Vilalta, R., Apte, C., Weiss, S. (2000). Operational Data Analysis: Improved Predictions Using Multi-computer Pattern Detection. In: Ambler, A., Calo, S.B., Kar, G. (eds) Services Management in Intelligent Networks. DSOM 2000. Lecture Notes in Computer Science, vol 1960. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44460-2_4
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DOI: https://doi.org/10.1007/3-540-44460-2_4
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