A Comprehensive Review of AI Techniques for Resource Management in Fog Computing: Trends, Challenges, and Future Directions

Abstract

Fog computing (FC), extending cloud services to the network edge, has emerged as a key paradigm for low-latency applications like the Internet of Things (IoT). However, efficient resource management, task scheduling, and load balancing pose challenges in fog environments. This review surveys recent research efforts aimed at addressing these challenges and optimizing FC performance. We conducted a systematic analysis of relevant research papers on FC published in reputable academic databases. The review focused on studies published between 2019 and 2024 and emphasized artificial intelligence based studies exploring resource management, task scheduling, and load balancing techniques within the FC domain. The review identifies a diverse range of techniques applied to optimize FC performance. These include machine learning (ML) and deep learning (DL) for resource allocation, heuristic algorithms for task scheduling, and nature-inspired meta-heuristics for load balancing. The review evaluates the strengths and limitations of these approaches, highlighting their impact on metrics like latency, energy consumption, and Quality of Service (QoS). This review demonstrates the significant progress made in optimizing FC through innovative techniques. ML and meta-heuristics have emerged as promising approaches for resource management, task scheduling, and load balancing, respectively. However, challenges persist in areas like real-world implementation complexities and ensuring service quality across geographically distributed fog networks. Future research directions are identified, emphasizing the need for further exploration of these challenges and the integration of emerging technologies like deep reinforcement learning for enhanced FC performance.

Keywords

• Fog computing
• artificial intelligence
• resource management
• optimization
• challenges