An Efficient Traffic Steering for Cloud-Native Service Function Chaining
Boutheina Dab, Ilhem Fajjari, Mathieu Rohon, Cyril Auboin, Arnaud Diquelou
Abstract
5G stakeholders make every effort to efficiently handle the unprecedented services and traffic demand. Particularly, Telco operators aim to deploy a cost effective network services within their next generation mobile networks. By leveraging cloud-native architectures, the stringent 5G services requirements can be satisfied while reducing both CAPEX and OPEX. Concretely, the adoption of microservice software design enables the disaggregation of Telco applications into small independent cloud-native network functions. Theses microservices, deployed as containers, are lightweight and perform in an independent way. However, in spite of its various benefits, microservice architecture raises new challenges to deal with in the network function virtualization (NFV) ecosystem. Typically, the service function chaining (SFC) of cloud-native network functions becomes extremely complex and error-prone due to the high number of microservices. In this paper, we address the problem of 5G traffic steering for cloud-native SFC in microservice based NFV ecosystem. Specifically, we envision and deploy an SDNless SFC framework ensuring efficient traffic steering between cloud-native network functions. Besides, we propose an optimized network-aware load balancing algorithm capable of carrying efficiently the traffic while considering the underlying infrastructure network state. Based on robust experiments conducted in the deployed Kubernetes-based platform, the results show the efficiency of our strategy in terms of deployment time and network performance.