Fault Estimation for Operational Systems

Abstract

Operational systems are crucial for corporations. A majority of the business processes flow through these systems and even minor downtimes on these systems may cause serious financial consequences. System logs are a promising way for analyzing the behaviors of operational systems. This work investigates fault estimation on a real-life data set derived from the system logs of a large-scale insurance company. Data set consists of operational system indicators like visit duration, connection properties and time of connection collected over four months. Regression and classification algorithms have been used to estimate the impact of the system and environmental parameters on the system response time. The best performance is obtained with the CatBoost classification, which yields 99% accuracy in estimating whether system responds within normal interval. This study assists the operational team in identifying problem scenarios, future improvements may be possible as logs from other operational systems from the company are considered using transfer learning. © 2022 Elsevier B.V., All rights reserved.