Service Outages Prediction through Logs and Tickets Analysis

Abstract

Service outage or downtime is a growing challenge to the service providers and end users. The major cause for the unavailability firstly is failure of equipments and applications at various places and secondly failure for proactive diagnosis and rectification. The system activities that are logged and the response of customers and providers in the form of trouble tickets could be studied for minimizing network faults. The downtime can be reduced when the failures are predicted well in time and proactively corrected. Accurate prediction of faults helps in responding to downtime even before the customer tickets are raised or network trouble is encountered. Most of the research focuses on trouble shooting through forecasting the quantity of trouble tickets using the historical ones. If these tickets can be supported with the warning in the form of Syslogs and the technical support of network tickets the predictive models would be more efficient and accurate. Dynamic and truly adaptive machine learning algorithms are essentially required for processing the torrent of data and formulating predictions based on the trends and the patterns existing in it. The work refers to i) identifying number of trouble tickets that are related to the device a few days before the network component fails, predicting fault will occur in broadband networks. Lasso and Ridge regression are used for the first and Bayesian structural time series analysis and prophet are used for the latter.