In which scenario is edge computing beneficial in hybrid architectures?

Edge computing is particularly beneficial in scenarios that require real-time data processing, such as Internet of Things (IoT) applications. This is because edge computing allows data to be processed closer to the source, reducing latency and enabling quicker responses. In an IoT setup, for instance, devices generate vast amounts of data that often need immediate analysis for functions like monitoring, control, and automation. By processing this data at the edge, you minimize the time it takes to send data to a centralized cloud and then receive a response, which is critical for applications where every second counts, such as in autonomous vehicles, industrial automation, or real-time analytics in smart cities.

In contrast, scenarios involving historical data analysis do not require immediate processing and can effectively leverage the capabilities of cloud services for extensive computation without the need for the immediacy that edge computing provides. Traditional web hosting services typically do not benefit from edge computing since they rely on server-side resources and less on immediate, localized data processing. Lastly, when data can be processed in a centralized cloud, edge computing becomes redundant, as the benefits of proximity and low-latency interactions are lost in a centralized model. Therefore, for applications demanding speed and responsiveness, edge computing is essential.