SFC Self-Modeling and Active Diagnosis

Abstract

The 5G system should be flexible to cope with diversity of functions and infrastructure, in an efficient way while supporting a wide range of services associated with a set of requirements through Service Function Chaining (SFC). Fault management plays a paramount role to fulfill the SFC requirements towards reliability of telecommunication networks. Model-based (MB) approaches are knowledge-based systems that reason about a system from an explicit representation of its structure and functional behavior through a dependency model. MB techniques solve novel diagnosis problems and provide explanations for their decisions. However, current MB methods suffer from some limitations due to network virtualization challenges: lack of network visibility and dynamic topologies. To tackle those limitations, we propose a self-modeling approach and an active diagnosis process for virtual networks that considers two types of knowledge to build the model: acquired knowledge and learned knowledge provided by fault injection to expand and validate the proposed model. The experimental results from their application to a real-world virtual IP Multimedia Subsystem (vIMS) use case show that the self-modeling and the active diagnosis procedures are effective in determining the root cause(s) of a failure and explaining fault propagation.