AI-Driven Autonomous Systems for Optimal Management of Fog Computing Resources

Fog and edge computing bring computing, network, and storage services closer to the source of data, effectively bridging the gap between cloud infrastructure and end devices. By shifting data processing, analytics, storage, and networking closer to host devices, this technology enables real-time data processing and decision-making. Even though fog and edge computing have fewer resources compared to cloud systems, they play a crucial role in time-sensitive applications. They offer location awareness, support for user mobility, real-time interactions, low latency, high scalability, and interoperability. The increasing number of IoT applications and the resource constraints in these environments have made efficient resource management essential. This has led to the adoption of AI and machine learning. AI-driven autonomous systems optimize resource provisioning, application deployment, task placement, and service management, thereby enhancing the overall efficiency and performance of fog computing systems. This paper delves into the fog computing system, exploring the integration of AI and highlighting the significant opportunities it presents for improving distributed computing systems.