What is described as a distributed computing model that processes data near its source?

The term that describes a distributed computing model that processes data near its source is edge computing. This approach brings computation and data storage closer to the location where it is needed, which minimizes latency and bandwidth use. By processing data at or near the "edge" of the network—where data is generated—it allows for faster decision-making and reduces the amount of data that needs to be sent to centralized data centers. This is particularly beneficial for applications that require real-time data analysis, such as IoT devices, autonomous vehicles, and smart sensors.

Cloud computing, while also a distributed model, typically refers to centralized data processing in remote servers, which does not prioritize proximity to the data source. Grid computing involves a network of computers working together to perform large tasks but can often still be centralized in nature. Centralized computing refers to a model where processing and data storage occur in a single location, negating any advantages of decentralized data processing. Thus, edge computing is essential for scenarios where speed and efficiency near the data source are paramount.