Machine Learning Approaches to Early Fault Detection and Identification in NFV Architectures

Abstract

Virtualization technologies become pervasive in networking, as a way to better exploit hardware capabilities and to quickly deploy tailored networking solutions for customers. But these new programmability abilities of networks also come with new management challenges: it is critical to quickly detect performance degradation, before they impact Quality of Service (QoS) or produce outages and alarms, as this takes part in the closed loop that adapts resources to services. This paper addresses the early detection, localization and identification of faults, before alarms are produced. We rely on the abundance of metrics available on virtualized networks, and explore various data preprocessing and classification techniques. As all Machine Learning approaches must be fed with large datasets, we turn to our advantage the softwarization of networks: one can easily deploy in a cloud the very same software that is used in production, and analyze its behaviour under stress, by fault injection.

Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey

“…Elmajed, Arij, Armen Aghasaryan, and Eric Fabre et al [73] presented a machine learning-based anomaly detection algorithm focusing on two main challenges to identifying the anomaly in the NFV network: first, to detect faults before they severely affect the network, and secondly, to take countermeasures before the unavailability of NFV services. For this purpose, an experimental cloud-based NFV application was created that is isolated from all other applications, and this environment contains few virtualized network functions.…”

“…Arij Elmajed [73] proposed a runtime solution for anomaly detection and focused on two main tasks: detecting anomalies before they affect system performance and taking timely countermeasures. Arij Elmajed [73] implemented his method using four different machine learning algorithms and studied their behavior in terms of accuracy. Girish and Dr. Sridhar [74] used the isolation forest algorithm technique to identify anomalies in the NFV network and create a decision tree for data.…”

Predicting Network Hardware Faults through Layered Treatment of Alarms Logs

“…Finally, in [ 19 , 20 ], we have two other examples of research works that addressed early fault detection and localization problems based on numerous real-valued KPI measurements by leveraging correlations between these metrics. Although in a different technological context and with different types of data (alarms as opposed to KPIs), we have also considered correlation patterns as an important source of information for tracking the changes in the systems with respect to fault occurrences in modules.…”