Self-Modeling based Diagnosis of Services over Programmable Networks

In this paper, we propose a multi-layer self-diagnosis framework for networking services within SDN and NFV environments. The framework encompasses three main contributions: 1) the definition of multi-layered templates to identify what to supervise while taking into account the physical, logical, virtual and service layers. These templates are also finer-granular, extendable and machine-readable; 2) a self-modeling module that takes as input these templates, instantiates them and generates on-the-fly the diagnosis model that includes the physical, logical, and the virtual dependencies of networking services; 3) a service-aware root-cause analysis module that takes into account the networking services' views and their underlying network resources observations within the aforementioned layers. We also present extensive simulations to prove the fully automated, finer granularity and reduced uncertainty of the root cause of networking services failures and their underlying network resources.