K
Kathleen Martin
Guest
Edge computing is easy to sell but hard to define. More a philosophy than any single architecture, edge and cloud are on a spectrum, with the current cloud service model often dependent on in-browser processing, and even the most edgy deployments reliant on central infrastructure.
The philosophy of edge, as most Reg readers know doubt know, is to push as much processing and compute as close as possible to the points of collection and utilisation.
If biology is any guide, edge computing is a good evolutionary strategy. The octopus has a central brain, but each tentacle has the ability to analyse its environment, make decisions and react to events. The human gut looks after itself, with roughly the same processing power as a deer, while both eyes and ears do local processing before passing data back. All these natural systems confer efficiency, robustness and flexibility: attributes that IT edge deployments should also expect.
But those natural analogies also illustrate another of edge's most important aspects – its diversity. 5G is often quoted as the quintessential edge case. It owes most of its potential to being designed around edge principles, moving the decision-making about setting up and managing connections into distributed control systems. The combination of high bandwidth, low latency, traffic management through prioritisation, all across moving targets, just can't work unless as much processing as possible takes place as close to the radios (and thus the users) as possible.
Move it
But another high-profile edge application, transport, needs a very different approach. An aircraft can generate a terabyte of performance and diagnostic data on a single flight, which outstrips the capabilities of in-flight datacomms.
Spread that across a fleet in constant global flux, and central control isn't an option. Autonomous processing onboard, prioritising of immediate safety information such as moment-to-moment engine parameters for available real-time links, and efficient retrieval of bulk data when possible, lead to design decisions far removed from 5G engineering.
Continue reading: https://www.theregister.com/2021/09/21/future_of_edge_computing/
The philosophy of edge, as most Reg readers know doubt know, is to push as much processing and compute as close as possible to the points of collection and utilisation.
If biology is any guide, edge computing is a good evolutionary strategy. The octopus has a central brain, but each tentacle has the ability to analyse its environment, make decisions and react to events. The human gut looks after itself, with roughly the same processing power as a deer, while both eyes and ears do local processing before passing data back. All these natural systems confer efficiency, robustness and flexibility: attributes that IT edge deployments should also expect.
But those natural analogies also illustrate another of edge's most important aspects – its diversity. 5G is often quoted as the quintessential edge case. It owes most of its potential to being designed around edge principles, moving the decision-making about setting up and managing connections into distributed control systems. The combination of high bandwidth, low latency, traffic management through prioritisation, all across moving targets, just can't work unless as much processing as possible takes place as close to the radios (and thus the users) as possible.
Move it
But another high-profile edge application, transport, needs a very different approach. An aircraft can generate a terabyte of performance and diagnostic data on a single flight, which outstrips the capabilities of in-flight datacomms.
Spread that across a fleet in constant global flux, and central control isn't an option. Autonomous processing onboard, prioritising of immediate safety information such as moment-to-moment engine parameters for available real-time links, and efficient retrieval of bulk data when possible, lead to design decisions far removed from 5G engineering.
Continue reading: https://www.theregister.com/2021/09/21/future_of_edge_computing/
