If it happens at all, edge computing just might become more important than cloud. Not so much because there would be a ton of stuff hosted there, because the majority of application code won’t be. Not because of explosive growth in things like self-driving cars, because they won’t contribute much to the edge for years./ Because there could be just enough stuff to change the cost dynamic in favor of broader adoption of cloud computing. A little edge could pull through a lot of cloud.
There’s a common view that cloud computing has an inherent cost advantage over data center computing because of “economy of scale”. That’s not true in most cases. The inherent economy of a resource pool generally tracks an Erlang curve, meaning that as the size of the pool grows you reach a point where additional scale no longer improves efficiency. In other words, there’s a point where data center size is such that when you factor in cloud provided profit margins, the cloud isn’t cheaper.
My own modeling has been pretty consistent in saying that about 23% of applications would be cheaper to run in the cloud at prevailing cloud pricing. About 70% of them are already there, and today most of public cloud growth comes not from “moving” to the cloud, but from transforming application front-ends, meaning GUI and related elements, to take advantage of cloud elasticity under load and resilience.
Suppose we wanted to increase the use of cloud computing, which certainly the cloud providers do, and likely other symbiotic groups as well. One way to do that would be to promote those front-end benefits by creating an application framework that could deliver more value for the same dollars. Another would be to somehow change the Erlang outcome. Edge computing could do both.
Let’s do the Erlang stuff first, because it’s the easiest to talk about. Here’s a simple principle to consider: There’s no such thing as a centralized edge. Resource pools are “pools” only if they’re built in such a way as to essentially eliminate differences in cost and performance among the members of the pool. When you spread out a set of resources, you lose the ability to leverage facility and operations efficiencies, you can no longer grab any resource and get the same result because of latency, and you can’t match application needs to remaining server capacity as easily.
Know what kind of applications got moved to the cloud the fastest? Applications that ran on dedicated, distributed, servers rather than in the data center. Server consolidation, in other words. Edge computing is a model for server “un-consolidation”, for explicit distribution of compute resources necessitated by the value of having them proximate to the data they’re processing.
There are a lot of reasons to move processing to the edge, including a desire to reduce latency. IoT and virtual/augmented reality applications need edge computing of some sort, and while it would be possible (and in some cases even advisable) to self-host these edge applications, there could be a considerable economy of scale gained by pushing the processing back a bit so that resources for a whole geography of users could be combined into a pool.
The reason for that is that the Erlang curve is steepest at the start, meaning that a small increase in the size of a resource pool yields a big benefit in cost efficiency. An important edge application might need server redundancy if you self-host, and some form of local technical support. Both those can be provided automatically out of a shared-edge-hosting service. However, there are some critical considerations in just where that shared hosting is located.
The limiting factor, even financially, in sharing edge computing is latency, which translates to the size of the geography from which a given shared-edge complex draws its users. What you’d like is to draw users from an area as large as latency permits, so that your edge complex is high on the Erlang curve and can replace user-hosted edge resources by offering a good price, but still earn a good profit. As the prospective service area gets bigger, not only does the latency increase but the incremental benefit in economy of scale is smaller because of the Erlang relationship. In short, you don’t have a more efficient edge resource, and you offer the user less.
This doesn’t address the question of whether there are credible edge applications in the first place, of course. Most of the discussion about edge computing has centered on IoT, and in fact on a combination of 5G and IoT. Every technology element you add to a mission adds a cost that demands a compensatory benefit. Every company doesn’t have an IoT application. Every IoT application doesn’t demand general-purpose edge computing. 5G for IoT needs its own justification. We need to expand our thinking here.
One possible expansion is to think of the “edge” as less a place that data center stuff moves forward into, than perhaps where user stuff moves backward into. We can see an example today, in the expanding interest in Chromebooks. A Chromebook is a lightweight edge element that relies on cloud/web processes for the heavy lifting. It’s almost a programmable dumb terminal, and so what it does is to cede what’s usually done on a PC to a cloud application. Could some of the things that a Chromebook does work in partnership with the edge rather than the cloud?
Another question is whether some of the “cloud” stuff needs to be distributed further out. I’m sure you’ve noticed that cloud providers have been working on their own edge strategies, some of which involve partnerships with network operators to get access to suitable edge hosting sites, and some of which involves architectural extension of cloud middleware (web services) to the premises, so that users who adopt edge hosting aren’t abandoning the cloud to do it. Surely this means that cloud providers see a risk that some cloud applications might migrate to the user edge, which means they think there are already candidates.
Probably the biggest potential application for the edge, the thing that could be most decisive in shifting more emphasis to the cloud overall, is virtual/augmented reality applications aimed at worker productivity. These offer the potential to launch a new wave of IT spending, the fourth such cycle since the dawn of business computing.
I’ve attempted to model this, so far exclusively for the US market where I have a lot of previous modeling work I can draw on. If we assume that a full IT spending cycle takes 10 years, which is roughly consistent with past cycles, then the total potential total-cycle business spending impact of IoT and VR/AR on productivity would be over $900 billion. The portion of that which could be assigned to cloud/edge services would be about $250 billion, and the annual spending for the peak period of 2025-2028 would be over $34 billion. I estimate that almost 80% of this new spending would be edge spending.
This would be a pretty decent pot for cloud providers to chase on its own, but that’s not the end of it. The new productivity cycle would be drawing on current applications, some parts of which (the front-ends) are already in or moving to the cloud, and the remainder still in the data center. If we were to add in this new-cycle productivity application set, it would have the effect of shifting some current applications “edge-ward”, moving more of the current cloud components to the edge and more of the data center components to the cloud. It’s really difficult to model this process, but based on what I have so far, I would expect that the net effect would be to increase cloud/edge spending by another $79 billion in each of those peak years. That would mean a new cloud revenue opportunity of $113 billion per year, which would be enough to transform the public cloud leader board if it were distributed differently than today’s cloud spending, which it likely would be.
All of this depends on realizing what I’ve called “point-of-activity empowerment” in prior blogs. In short (you can do a search on the blog page to find other references to the term), this combines VR/AR technology with IoT and “contextual analysis” of user mission and location to provide workers with assistance wherever they happen to be, always in the context of what they’re trying to do.
That’s the reason why edge impact on cloud spending has to be qualified by the “if it happens” I opened with. There’s an enormous build-it-and-they-will-come bias in tech, a notion that resource availability fills pools with applications like petri dishes grow bacteria. It takes more than that; applications that either improve productivity for businesses or quality of life for consumers are the basic engine of technology change, and so they will be with the edge. Who drives all of this? My bet is on the cloud providers because they simply have the most to gain.