Design of federated learning-based resource management algorithm in fog computing for zero-touch network

Abstract

The concept of zero-touch networking involves creating networks that are fully autonomous and require minimal human intervention. This approach is increasingly relevant due to the rapid growth of current cloud architectures, which are beginning to reach their limits due to continuous expansion demands from users and within the network core itself. In response, Fog computing, acting as a smart, localized data center closer to network nodes, emerges as a practical solution to the challenges of expansion and upgrading in existing architectures. Fog computing complements cloud technology. However, the realization of zero-touch networks is still in its early stages, and numerous challenges hinder its implementation. One significant challenge is the NP-hard problem related to resource management. This paper introduces an optimal resource management algorithm based on Federated Learning. The effectiveness of this algorithm is evaluated using the iFogSim simulator within the existing cloud-fog architecture. The results demonstrate that the proposed architecture outperforms the current infrastructure in several key aspects of resource management, including system latency, number of resources processed, energy consumption, and bandwidth utilization.