Most organizations are moving at least some of their applications to the cloud, to reduce cost and take advantage of open, flexible, cloud-native software platforms. However, the big advantage of the centralized cloud – the efficiency of aggregating all data and applications in one scalable pool – is also its disadvantage. Transporting every piece of data over long distances leads to high connectivity costs, and unacceptable round-trip times for low-latency applications. But there is an alternative.
Near-real time response is important if applications enabled by AI, the IoT, or virtual reality are to fulfill their potential. That will be best achieved by combining compute and storage capabilities with high speed connectivity, close to the user. That keeps the applications and analytics close to the consumer and the data source, enabling latencies below 20ms.
That, in turn, supports a wide range of key enablers for high quality services, such as:
Intelligence at the edge enables vast amounts of data generated locally, such as those from IoT devices or cameras, to be pre-processed so only a small subset of such data is transmitted to a central data center or to the Cloud. This can reduce backhaul connectivity requirements, and signaling load, significantly in many enterprise scenarios. In addition, the organization can continue to function if connection to the data center or central cloud is lost.
Many industries are interested in deploying edge compute, combined with high quality connectivity throughout their location, resulting in a self-contained, highly reliable cloud platform to support their specific requirements. Smart factories, trains, hospitals, stores and entire cities are examples.
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