Adaptive Learning for Service Monitoring Data

Abstract

Service monitoring applications continuously produce data to monitor their availability. Hence, it is critical to classify incoming data in real-time and accurately. For this purpose, our study develops an adaptive classification approach using Learn++ that can handle evolving data distributions. This approach sequentially predicts and updates the monitoring model with new data, gradually forgets past knowledge and identifies sudden concept drift. We employ consecutive data chunks obtained from an industrial application to evaluate the performance of the predictors incrementally.