Fog Computing

• Fog computing is a decentralized computing architecture that extends cloud computing capabilities closer to the network edge and locations where data is produced or gathered. Unlike traditional cloud computing, which relies on centralized data centers for data processing, fog computing distributes processing, storage, and networking resources across various devices, including sensors, edge devices, local servers, and cloud resources.

• This architecture is useful in AI distributed processing, where the computation and communication of AI models are spread across multiple nodes or devices. Distributed AI uses parallel processing and collaborative computing to enhance scalability, improve stability, and efficiently handle complex tasks. However, it introduces challenges such as managing communication overhead and ensuring synchronization.

• The fog computing system comprises three layers: cloud layer, fog layer, and edge layer. Each layer has specific functions and components that interact to optimize data processing and management, especially in the context of AI and Internet of Things (IoT) applications.

• The following table describes the components of a fog computing system.

Layer	Components	Functions
Cloud layer	Cloud servers	Centralized infrastructure for high-level processing, long-term storage, and complex analytics. Communicates with fog servers.
Fog layer	Fog servers	Acts as a bridge between cloud and edge layers, provides decentralized processing closer to data sources, and reduces latency and bandwidth usage.
Edge layer	End-user devices and data sources (users, cameras, sensors, smartphones, access points, onboard units)	Closest to data sources; processes data locally or sends it to the fog layer for further processing.

• The following figure displays the fog computing system components and the characteristics of each layer regarding the AI distributed processes.

Alt text

• Fog computing enables real-time, low-latency processing of data generated by IoT devices, autonomous vehicles, and industrial systems. By bringing computational power closer to the edge, fog computing allows AI models to be trained, inferenced, and deployed efficiently at or near the source of data generation.

• The following list describes the key benefits of fog computing for distributed AI:

  • Reduced latency: Data are processed locally, which is crucial for real-time applications like autonomous driving and industrial automation.

  • Bandwidth efficiency: Data sent to the cloud are limited to conserve bandwidth and reduce costs in bandwidth-constrained scenarios.

  • Enhanced security: Sensitive data are processed at the edge, which improves privacy and security before cloud transmission.

  • Scalability: Distributed AI processing occurs across nodes, which scales resources dynamically for varying workloads.

  • Improved reliability: Tasks are distributed across fog nodes, ensuring continuous operation even if a node fails.

  • Real-time decision making: Immediate actions are supported in time-sensitive applications because data are processed where they are generated.