Association Mining and Prediction of SystemPerformance Attributes in a large-scale ITinfrastructure

Sammanfattning: The master thesis seeks to establish relationships between eleven system performance metrics in a large-scale IT infrastructure, and to predict their future behavior. The rst task was performed using multiple linear regression, with one of the eleven metrics being taken as a dependant variable measuring total system performance. Results of regressions run over variations of the source data were evaluated, and two of the metrics were concluded to be of consistently high ability to explain the variability in the metric measuring total system performance. The second task of prediction was approached by attempting to replicate the results from another research paper which presented a similar problem and relevant results. A Kalman lter calibrated by expectation maximization was used alongside vector autoregression to evaluate the possibility of doing predictions. The results were not found to be of obvious practical use, with the exception of the vector autoregression procedure which highlighted regularities in the metrics. When unifying the sampling rate of the performance metrics, additional descriptive statistics over the sampling intervals were extracted when downsampling in order to retain potentially useful information, which was found by manual inspection in the case of one of the metrics. The inclusion of these additional statistics was, however, not found to have a positive impact in the regression analysis or in the prediction attempts.