Self-Modeling Based Diagnosis of Software-Defined Networks

TL;DR

This paper introduces a self-modeling diagnosis approach for SDN and NFV networks using Bayesian Networks, enabling detailed, runtime root cause analysis to improve network resilience and robustness.

Contribution

It presents a novel self-modeling diagnosis method with granular templates and Bayesian Networks for dynamic SDN and NFV environments.

Findings

Finer granular templates for detailed network component modeling

Runtime generation of diagnosis models from network templates

Validation of the Bayesian Network-based diagnosis approach

Abstract

Networks built using SDN (Software-Defined Networks) and NFV (Network Functions Virtualization) approaches are expected to face several challenges such as scalability, robustness and resiliency. In this paper, we propose a self-modeling based diagnosis to enable resilient networks in the context of SDN and NFV. We focus on solving two major problems: On the one hand, we lack today of a model or template that describes the managed elements in the context of SDN and NFV. On the other hand, the highly dynamic networks enabled by the softwarisation require the generation at runtime of a diagnosis model from which the root causes can be identified. In this paper, we propose finer granular templates that do not only model network nodes but also their sub-components for a more detailed diagnosis suitable in the SDN and NFV context. In addition, we specify and validate a self-modeling based diagnosis using Bayesian Networks. This approach differs from the state of the art in the discovery of network and service dependencies at run-time and the building of the diagnosis model of any SDN infrastructure using our templates.