Читать книгу Understanding Infrastructure Edge Computing - Alex Marcham - Страница 31

Particularly with emerging use cases such as distributed artificial intelligence (AI), the cost of transporting data from the device edge locations where it is generated to a data centre location where it can be processed in real time will present a growing challenge. This is not only a technical consideration where network operators must appropriately provision upstream bandwidth in the access and midhaul layers of the network, but there is also a significant operational expenditure (OPEX) and capital expenditure (CAPEX) burden on the network operator associated with overprovisioning long‐haul network connectivity.

Infrastructure edge computing aims to address this challenge by moving the locations at which large amounts of data can undergo complex processing, for example, by distributed AI inferencing, to a set of locations which are positioned closer to the sources of this data than with today’s centralised data centres. The shorter the distance over which the bulk of data must be transmitted, the lower the data transport cost can be for the network operator which allows any use case reliant on moving such large volumes of data to be more economical and thus more practical to deploy and operate.