Which feature distinguishes fog computing from traditional cloud computing?

Fog computing is distinct from traditional cloud computing primarily because it emphasizes location-specific data generation and processing. In fog computing, data is processed closer to the source – such as devices or sensors at the edge of a network – rather than being sent to centralized cloud servers for processing. This decentralization allows for quicker data handling, reduced latency, and improved response times, which are critical for applications like IoT (Internet of Things) where real-time processing is often required.

Location-specific data generation means that data can be contextualized based on where it is generated. For instance, temperature data collected from a sensor in a smart city can be immediately analyzed and acted upon locally to manage energy usage efficiently, without needing to send every piece of data to a far-off cloud server for computation.

The other options represent characteristics that do not uniquely identify fog computing. Centralized data processing is a hallmark of traditional cloud computing. Middleware functionality is not exclusive to fog computing, as middleware is widely used in various computing models, including cloud. Network storage optimization can apply to both fog and cloud settings but does not define fog computing specifically. Thus, focussing on the localized nature of data processing in fog computing highlights its unique advantages over traditional cloud systems.