Potential connectivity service provider roles in edge computing vary by chosen role, but also by the value of edge analytics.
It is fair to note that the advantage of edge computing in some cases hinges on how much analytics contributes to the value of sensor data, in real time or near real time. In essence, the broad choices are processing at the edge, in the cloud (at a remote location) or using a hybrid approach.
By definition, edge analytics adds value when the analytics are necessary for real-world processes that are very dynamic. On the other hand, remote processing might make more sense when learning does not have very-dynamic implications, and when the amount of raw data transmitted to the remote data centers is reasonable.
The hybrid approach (some local analytics, some remote analytics) makes sense in scenarios where decisions and response time are optimized, but network data load also is reduced.
In other words, even if analytics run many times faster at the far edge, there can be latency when the amount of raw data to be crunched is very high (analysis of video feeds, for example), as well as transmission cost implications.
But analytics location also is controlled by the application setting. Engine performance of a motorcycle perhaps cannot easily be conducted anywhere but at a remote location. But data might be collected and then transmitted in non real time using store and forward.
On the other hand, data related to road hazards or mechanical condition of the brakes might have to be displayed and processed locally to have any immediate value.
Local analytics might also make sense if the edge device has the ability to handle the amount of local processing, and if the device already is fully paid for, thus avoiding recurring transmission costs.
Longer-term analytics might then be performed by cloud data centers that have gotten the records non real time.
Telcos and other connectivity service providers arguably have the greatest value in edge computing when hybrid computing is optimal. The edge processing centers might offer small value when the edge devices can process real time data themselves.
Likewise, edge computing for analytics provides small value when data can be processed remotely, in non real time, by cloud data centers. Arguably, the highest value is provided by edge computing facilities when end user devices do not have the ability to process in real time but when near real time analytics are valuable.
Connectivity service providers can, in principle, choose from various business roles and revenue models of varying risk and value proposition. The lowest risk approaches tend to have lowest value, the highest risk approaches the highest value, as elsewhere in the information technology ecosystem.
Service providers can choose to create and operate dedicated edge hosting facilities, where the telco owns and manages edge-located compute/storage resources that are connected to the telco network. The customer runs its software or applications. This is similar to a colocation or hosting revenue model.
In other cases, the edge computing provider might choose to operate edge infrastructure or platform “as a service.” This is the edge version of the AWS cloud computing model.
Also, a connectivity provider can choose to operate as a system integrator, providing turnkey information systems that require an edge computing component.
Connectivity providers might choose to develop their own edge-computing-based solutions for enterprises or other organizations,
Finally, some connectivity providers might develop end-to-end consumer retail applications based on edge computing support, such as virtual reality for live sports events, for example.
No matter which approaches are chosen, attempting to have a greater role in edge computing, beyond supplying connectivity, will make sense for larger service providers. Arguably, even the lowest-value edge computing role has more value than the connectivity role alone.
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