FedFog - A federated learning based resource management framework in fog computing for zero touch networks

Abstract

Fog computing offers an optimal answer to the expansion challenge of today’s networks. It boasts scaling and reduced latency. Since the concept is still nascent, many research questions remain unanswered. One of these is the challenge of Resource Management. There is a pressing need for a reliable and scalable architecture that meets the Resource Management challenge without compromising the Quality of Service. Among the proposed solutions, Artificial Intelligence based path selection techniques and automated link detection methods can provide lasting and reliable answer. An optimal approach for introducing intelligence in the networks is the infusion of Machine learning methods. Such futuristic, intelligent networks form the backbone of the next generation of Internet. These self-learning and self-healing networks are termed as the Zero-Touch networks. This paper proposes FedFog, a Federated Learning based optimal, automated Resource Management framework in Fog Computing for Zero-touch Networks. The paper describes a series of experiments focusing on Quality of Service parameters such as Network latency, Resources processed, Energy consumption and Network usage. The simulation results from these experiments depict superiority of the proposed architecture over traditional, existing architecture.