SDN-Based Multi-Objective Optimization for Task Offloading with Algorithm Federated Learning in Fog Computing Environment

Abstract

Due to the substantial volume of data associated with the IoT, processing and storing such a large amount of data is not easily feasible. Nevertheless, many of its applications face challenges in cloud computing, such as latency, location awareness, and real-time mobility support. Edge computing helps provide solutions to these challenges. In this article, the MINLP path optimization problem is initially addressed using SDN, SA+GA, OLB-LBMM, and Round-Robin methods. Subsequently, based on the obtained results, the SDN method, which has achieved the best outcomes among the approaches, is selected. This article involves a simulation of the Internet of Things network for optimal allocation of shared resources in edge computing. The network architecture comprises five distinct layers, including cloud services, the SDN controller, edge computing nodes, edge computation and users. The algorithm employed in this problem is the federated learning and stochastic gradient descent algorithm. It selects the optimal edge node for user service provision through two learning and training phases, aiming to allocate shared resources with the goal of optimizing three parameters: cloud service providers' revenue, average latency, and user satisfaction. This algorithm is compared with several other methods. The selected model and algorithm, in comparison with other algorithms used in solving similar models, lead to a centralized management system, the implementation of effective network management, and the utilization of various communication media. This approach ensures timely access to services, contributing to increased profits for providers and user satisfaction.