Predictive Intent Maintenance with Intent Drift Detection in Next Generation Network

TL;DR

This paper introduces an unsupervised learning-based algorithm for detecting intent drift in Intent-Based Networking, enabling predictive maintenance of network intents to prevent degradation and failure.

Contribution

It proposes a novel intent drift detection method using multiple unsupervised learning techniques and identifies DBSCAN as the most effective model for this purpose.

Findings

DBSCAN outperforms other models in detecting intent drifts.

Affinity Propagation shows the poorest accuracy and latency.

The approach enhances intent maintenance in IBN systems.

Abstract

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 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 an intent drift detection algorithm for predictive maintenance of intents which can use various unsupervised learning techniques (Affinity Propagation, DBSCAN, Gaussian Mixture Models, Hierarchical clustering, K-Means clustering, OPTICS, One-Class SVM), here applied and comparatively analyzed due to their simplicity, yet efficiency in detecting drifts. The results show that DBSCAN is the best model for detecting the intent drifts. The worst performance is exhibited by the Affinity Propagation model, reflected in its poorest accuracy and latency values.