Intelligent Resource Orchestration for Fog-Edge Computing in Software-Defined Networks: A Deep Reinforcement Learning Approach

Fog and edge computing architectures integrated with software-defined networking (SDN) have emerged as transformative paradigms for deploying ultra-low latency applications in distributed systems. This paper introduces an intelligent resource orchestration framework that revolutionizes computational offloading and resource allocation across fog and edge nodes through advanced deep reinforcement learning techniques. By synergizing Lyapunov optimization with deep Q-networks (DQN) and leveraging SDN's programmable control plane, our approach achieves unprecedented reductions in service latency and energy consumption while guaranteeing strict quality of service (QoS) requirements. The framework employs a novel hybrid optimization model enhanced with deep learning approximations for exceptional scalability in large-scale fog-edge networks. Comprehensive simulations on heterogeneous edge-cloud topologies demonstrate that our method dramatically outperforms state-of-the-art approaches, achieving groundbreaking improvements of up to 68% in latency reduction and 52% in energy efficiency across diverse workload scenarios, establishing new benchmarks for intelligent resource management in distributed computing environments.