A Machine Learning Based Management System for Network Services

Providing high quality and uninterrupted network service is becoming crucial for service providers. In this paper, we present an approach to quantify and indicate service quality based on the topology state transitions from the perspective of network service provider. Building our model as a Finite State Machine (FSM), we show novel application of machine learning (ML) classification algorithms to classify appropriate states for undefined input alphabets in FSM. In other words, we implement ML algorithms to extract both service states and possible root causes of service degradation only from measured certain Key Performance Indicator (KPI) values that are observed directly through network elements. We have implemented our network topology state classification approach using the dataset obtained in Graphical Network Simulator 3 (GNS3) simulation environment, and performed measurements to evaluate classification accuracy of different algorithms. Additionally, we have identified priority of relevant KPIs impacting the service quality. Our results indicate that network topology state changes can be classified up to 88% accuracy and F1 scores using ensemble learning methods such as Gradient Boosting Classifiers.