Intelligent Agent support for Topology Learning in microservices-based SDN Controller

The softwarization of networks is increasingly spreading, and one of the main paradigms is SDN (Software-Defined Networking), which allows overcoming the limitations mainly arising from the integration of the control plane and the forwarding plane within the network devices. It extracts the control plane to place it within a new logically centralized component: the SDN controller. Since this is a monolithic architecture that limits reliability and scalability, distributed solutions based on microservices have been proposed in the literature. In parallel, Agents are fully intelligent, atomic, and autonomous decision-making units that can be flexibly recomposed to create a completely autonomous network system. They also have the ability to replicate single or multiple decision-making processes that collaborate with each other. The development of future networks such as 5G, including 6G, is pushing towards the concept of network management automation and integration of intelligence, making agents an excellent means to meet this trend. This paper first introduces intelligence in the form of agents to a distributed SDN controller based on microservices, by implementing two new functionalities: topology _learning and shortest path, Then, it leverages a microservices-based SDN solution based on Ryu SDN framework, named MSN, to run agents in a Docker Container environment. Multiple measurements were performed locally in a single machine. Results show the topology learning performances compared with several network topologies. Moreover, the shortest patti agent experimental evaluations show the knowledge size depends on the network topology and the performances of different algorithms.