Alternative implementations of two-level adaptive branch prediction
ISCA '92 Proceedings of the 19th annual international symposium on Computer architecture
Tracking long-term growth of the NSFNET
Communications of the ACM
Next cache line and set prediction
ISCA '95 Proceedings of the 22nd annual international symposium on Computer architecture
A system level perspective on branch architecture performance
Proceedings of the 28th annual international symposium on Microarchitecture
Semi-empirical multiprocessor performance predictions
Journal of Parallel and Distributed Computing
Predicting equity returns from securities data
Advances in knowledge discovery and data mining
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Parallel performance prediction using lost cycles analysis
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Forecasting network performance to support dynamic scheduling using the network weather service
HPDC '97 Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing
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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.