Predictive Intent Maintenance with Intent Drift Detection in Next Generation Network

Autor:	Muonagor, Chukwuemeka, Bensalem, Mounir, Jukan, Admela
Rok vydání:	2024
Předmět:	Computer Science - Networking and Internet Architecture
Druh dokumentu:	Working Paper
Popis:	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. Comment: This paper is uploaded here for research community, thus it is for non-commercial purposes
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.15091 Zobrazit plný text záznamu View this record from Arxiv