Resource Allocation and Load Balancing for Fog Computing Using Artificial Intelligence Techniques

Fog computing (FC) has emerged as a critical paradigm for enabling low-latency and efficient data processing in Internet of Things (IoT) environments. However, resource allocation and load balancing remain significant challenges due to the heterogeneous and dynamic nature of FC networks. This review systematically analyses recent advancements in artificial intelligence (AI)-driven approaches for optimizing resource allocation and load balancing in FC, with a focus on studies published between 2019 and 2024. The survey highlights the role of machine learning (ML), deep learning (DL), and reinforcement learning (RL) in intelligent resource allocation, ensuring efficient task offloading and execution. Additionally, it explores heuristic and nature-inspired meta-heuristic algorithms for dynamic load balancing, improving system throughput, energy efficiency, and Quality of Service (QoS). While these techniques have demonstrated significant improvements in FC performance, challenges such as real-world implementation complexity, scalability, and system heterogeneity persist. The review identifies future research directions, emphasizing the need for advanced AI-driven frameworks and deep reinforcement learning techniques to enhance resource management and load balancing in distributed FC