Operational Data Analysis: Improved Predictions Using Multi-computer Pattern Detection

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 availablerecords 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 di erent computer models exist and can be extracted to improve the accu...

• Type: Conference
• Year: 2000
• Where: DSOM
• Authors: Ricardo Vilalta, Chidanand Apté, Sholom M. Weiss
• Keywords: Applies Data-mining Techniques, Computer Networks, DSOM 2000, Linear Multivariate Model, Multivariate Linear Regression