An “edge cloud” refers to a middle ground between cloud computing and the edge of a network. This is often not a precise definition because the area between the edge and the cloud is ambiguous. While a cloud embodies core centralized compute and storage resources available via the internet, the edge of a network, sometimes more specifically described as the device edge, is a term that refers to the furthest physical and/or logical aspect from the core—for example, where there are IoT devices.
The edge can be described in other terms, by the burdens it places on a network. Specifically, the edge is an endpoint whereby latency, bandwidth, and network congestion become significant enough to warrant placing compute and storage resources closer to the data producing device rather than rely on backhauling data to the core for processing. Imagine several regional data center substations replacing a single central data center, commonly known as a distributed system.
In this way, the edge cloud is formed (the cloud core in the above example somewhat disappears by edging outward towards the device edge via multiple stations), and instead of a single brain with many very long latent connections, these edge devices now transmit data to the most available station (read closest or least congested).
Edge cloud is a common enterprise practice today, and may simply go by another name, redundant distributed systems. Companies that want to maintain a high level of available services, like streaming, cannot rely on a single central data center to meet demands, and so distribute their cloud content in strategic locations.
Yet, because it serves a purpose similar to the device edge, the edge cloud is different from redundant content delivery systems. The device edge challenges networks by introducing latency, bandwidth, and network congestion issues. Before IoT and the proliferation of devices, data was still generated in relatively low amounts. Today, these devices and other demands have overburdened the internet as a whole. Data transiting the internet is now exponentially more voluminous every year leading to the device edge challenges mentioned. The net effect is slower Internet infrastructure, and greater inefficiency for businesses that rely on the Internet for backhauling their data.
One solution is to avoid the data backhaul. For edge devices, placing storage and compute on the device, or local network, can eliminate the need to “fetch” compute power. For example, a small server in a smart home could collect and process device data, eventually relaying a summary back to the cloud. However, homes with their own servers may be overkill.
But, in smart cities, where the edge is more diffused, edge clouds find a use case. This diffusion has earned edge clouds another name, “fog”. In a fog, the physical edge falls off and the logical edge remains, for smart cities the logical edge may be the city limits, but edge devices can be everywhere. In these cases, maintaining focus on the purpose of edge computing is necessary: moving compute and storage resources closer to where the data is created offering local processing so as to reduce latency, bandwidth, and congestion issues.