Boosting Aided Approaches to QoS Prediction of IT Maintenance Tickets

摘要

Ticketing system is an example of a Service System (SS) which is responsible for handling huge volumes of tickets generated by large enterprise IT (Information Technology) infrastructure components, and ensuring smooth operation. The system maintains the provision of recording the time that reflects when a ticket is opened, acknowledged to user, resolved and/or closed, from which different QoS parameters could be obtained. For example, Resolution Time can be computed as the difference of resolution date and opening date of the ticket. One needs to use new technology solutions in QoS-related analysis like categorization of tickets according to their QoS, predicting QoS parameters for new tickets etc., to improve the performance of the SS. In this work we propose boosting oriented solutions to QoS prediction of tickets using crisp and fuzzy set models of QoS. In particular, we employ a two-stage analysis framework for QoS prediction for incoming tickets which includes clustering incident tickets based on QoS values and building a regression model using this categorization and the textual contents of tickets. We carry out experiments on industrial data sets using different techniques for prediction. We improve the quality of prediction by using suitable boosting techniques. We propose random forest boosting on Logistic Regression and gradient boosting on NNLS for our purpose, both of which improve the performance of prediction. We report these results and compare them.