Discussions about edge computing in the recent years reminds me of how far we have come in distributing computing and how many types of “edge” computing there are. There are many edges to consider, which I think of colloquially as: the Cloudy Edge, the Business Edge, and the Personal Edge. A more apt term for all this perhaps is proximity computing.

The Economics of Proximity Computing

First, a quick look at the economics of proximity computing, as we think of the different edges. Events in our world need a timely response either for good user experience (“changing TV channels”) or to avoid catastrophes (“gas line leak”).

We need to choreograph complex systems to Sense, Process, and Act (SPA)as events occur. The cost of SPA’ing is a function of local vs. remote processing costs, network connectivity costs and remote systems management costs.

Size and power aside, proximity computing balances the timeliness of responding to an event with the cost of that response.

The three edges — cloud, business, and personal edges — deploy SPA to a varying set of problems in different environments, for the purpose of optimal, automated response.

The Cloudy Edge

This is the least talked about Edge. Even before our team tried to put computing on a branch router, there was a network edge — a topological term in the service provider or enterprise network where traffic first entered from dial-up modems (yes!) in homes, or from remote branch offices. This was simply an SP network edge or an enterprise network edge without any computing capacity at the edge. They were called PoPs (points-of-presence).

The demand for application performance and content delivery required the network edge to add applications and data. Modern day Edge Data Centersfulfill that need. Content Delivery Networks (CDN) leverage them so that we get better page and video loads. This edge is now enhanced with Mobile Edge Computing, as mobile app performance needed to get better. We do it by colocating Radio Access Network (RAN) PoPs and applications.

So the old PoP got edgy with content and computing; SP edge, mobile edge, and enterprise edge came together to be the Cloud’s Edge.

The Business Edge

This is the most talked about Edge. Connected machines and people galore here. It is in our carpeted offices, in uncarpeted spaces, out in the open where we work and play. Many IoT discussions seem to presume that this is the only edge. Every IoT discussion extols the virtues of this Edge. So I’ll be brief. Mission-critical SPA goes on in this realm especially as Industrial IoT gathers momentum.

The Personal Edge

This Edge surrounds our person and is sometimes inside us. It is at home. It moves with us. Collectively this edge is mobile. The personal edge moves in and out of the business edge as we move. It comprises of home robots, glasses, smart pills, watches, home automation systems and so on. We will hear more about this edge in the next 5 years as intelligent home devices and personal devices proliferate.

Near, Far, Wherever You Are

21st century stream data processing enabled by pervasive computing and pervasive connectivity is constantly with us. We are being constantly SPA’d upon (sensed, processed, and acted upon). We will see more talk and action from players in these three edges as they innovate and adapt for new applications. As they do, remember to not let all this talk of edge computing get under your skin. It already is! Just chalk it up to proximity computing if that helps takes the edge off.

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The combination of fast and affordable computing and pervasive connectivity has turned data processing on its head. The approach to processing has gone from store-and-process to process-and-store. Couplethis with scale-out architectures for processing and we’re cooking…cooking data like we’ve never done before.

Pervasive Connectivity and Computing

Looking back, the blending of connectivity and computing that underlies both client-server and cloud-mobile opened up a plethora of options to combine compute and network to distribute intelligent processing. We are now able to optimize for costs and responsiveness and realize low-latency processing at the best cost.

We put these choices to work when we choose to develop a native mobile app vs. web/HTML5 mobile app, or when we decide how much processing Amazon Echo does at home vs. in the cloud.

Scaled Out Stream Processing

Taking advantage of this compute-network merger meant we could scale-out the processing required, not just across distributed data centers and cloud, but also to the edges where end-points like wearables, phones, and Alexa exist. Further, we are not processing data in batch, but streaming data (“data-in-motion”) before it is stored.

Our infrastructure now processes streaming data like humans do, on-the-fly. Not after the data is parked in some flash storage equivalent to our sub-conscious.

The net result of stream processing on a scaled-out infrastructure looks like this. We are used to calling it IoT.

If distributed artificial and human intelligence across a labyrinth of connected devices is IoT, so be it.

However, when at my command, Alexa streams Bach’s Cello Concerto performed by Yo Yo Ma, I won’t be thinking IoT. I’ll be thinking Streams ‘R’ Us. That is, if a comparison between biological information processing and computers enters my mind at all!

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