Performance Analysis of Learning-based Intent Drift Detection Algorithms in Next Generation Networks

Intent-Based Networking (IBN) is a known concept for enabling the autonomous configuration and self-adaptation of networks. One of the major issues in IBN is maintaining the applied intent due to the effects of drifts over time, which is the gradual degradation in the fulfillment of the intents, before they fail. Despite its critical role to intent assurance and maintenance, intent drift detection was largely overlooked in the literature. To fill this gap, we propose a learning-based intent drift detection algorithm for predictive maintenance of intents and analyze its performance by applying various unsupervised known learning techniques available as open-source (Affinity Propagation, DBSCAN, Gaussian Mixture Models, Hierarchical clustering, K-Means clustering, OPTICS, One-Class SVM). We apply these techniques for intent-drift detection and analyze them comparatively on their efficiency in detecting drifts. The results show that DBSCAN is the most efficient model for detecting the intent drifts. The worst performance is measured by the Affinity Propagation model, reflected in its poorest accuracy and latency values. To the best of our knowledge, this is the first work to address the problem of intent drift detection, and analyze its efficiency for intent maintenance.