Fog Computing extends cloud computing capabilities to the network edge, creating a distributed computing architecture that provides computation, storage, and networking services between end devices and traditional cloud data centers, optimizing the processing of data closer to its source.

For technical leaders, Fog Computing represents an architectural approach that addresses limitations of both purely centralized cloud models and fully decentralized edge processing. While edge computing typically focuses on immediate, localized processing at end devices, fog computing establishes an intermediate layer—sometimes called the “fog layer”—that aggregates resources from multiple network elements like routers, switches, access points, and dedicated fog nodes. This hierarchical model creates a continuum from edge to cloud, allowing workloads to be placed optimally based on their requirements for latency, network bandwidth, processing power, and data locality. Fog architectures enable sophisticated capabilities including local analytics on aggregated data streams, temporary storage for data in transit, protocol translation between diverse IoT devices and backend systems, and intelligent routing of information flows. Enterprise architects implementing fog computing must address several challenges: orchestration across heterogeneous fog nodes; security models that function across trust boundaries; resource management for constrained devices; and interoperability between fog implementations. Standardization efforts like those from the OpenFog Consortium (now merged with the Industrial Internet Consortium) aim to address fragmentation through reference architectures. For CIOs and CTOs, fog computing offers strategic value in scenarios where data volumes, latency requirements, or network constraints make purely cloud-based approaches impractical, particularly in industrial environments, smart cities, connected vehicles, and distributed retail operations where intelligent local processing can enhance reliability, responsiveness, and efficient resource utilization.