Towards explainable artificial intelligence for network function virtualization

Network Function Virtualization (NFV) refers to the process of running network functions in virtualized IT infrastructures as softwarized Virtual Network Functions (VNFs). Several telecom service providers are currently benefiting from this concept, as it enables a faster introduction of new network services, thereby meeting changing requirements. Following a trend initially adopted by cloud service providers, telecom service providers are also adopting de-aggregation of the VNFs into microservices (μservices). However, a μservice-based architecture that can manage a large set of diverse and sensitive network functions requires new Artificial Intelligence (AI)-based methodologies to cope with the complexity of the μservice-based NFV paradigm. This paper focuses on the use of explainable AI (XAI) for gradually migrating towards a μservices-based architecture in NFV. The paper first establishes the need for XAI to transform the NFV architecture to a μservice-based architecture and then describes some of our research objectives. Afterwards, our preliminary approach and long-term visions are provided.