Designing for the edge requires thinking differently, not just smaller
Despite priding itself on technical inventiveness, large segments of the technology industry are propelled by marketing, a field where it’s often better to be first than good and to ride hype rather than shed light. One of the latest areas in IT hampered by more fuzz than focus is ‘edge computing’. I use scare quotes since, as I mentioned in discussing Red Hat’s edge strategy, “the edge” isn’t a uniform category, but a spectrum of application types, system designs, deployment and infrastructure models and performance requirements with enormous variety. Such heterogeneity means that there will never be an easy, succinct way to describe edge systems, but I agree with Gartner’s Bob Gill (and many others) that a unifying quality is a need to directly and immediately process and act upon distributed sources of data at or very near the source.
Human nature being what it is, system and application designers tend to be lazy take the path of least resistance and apply existing mental models and implementation patterns to new problems, often with disastrous (at worst) or sub-optimal (at best) results. Edge infrastructure and applications are no different, where a common scenario — one endorsed and marketed by both enterprise systems vendors and cloud providers — entails putting small-scale replicas of an enterprise system and software stack in a remote location, whether that is a branch office, retail store, manufacturing site or managed hyper-local data center.
Facilities like Vapor IO’s INZONE, which I profile here, combine low-latency local connectivity with massive cloud- and carrier-neutral backhaul that makes them ideal locations for many edge implementations and many IT architects will use them like a local appendage of a cloud environment. However, extending cloud infrastructure and application designs to a wireless base station only addresses a subset of possible edge scenarios, nor does a ‘copy-downscale-and-paste’ approach to edge implementation represent the best design methodology.
While the word ‘edge’ denotes a boundary or discontinuity, edge computing looks more like a fractal, with more detail and different patterns emerging the closer you look at the extremities. Adding processing intelligence to formerly single-purpose devices represents a significant technology trend that usually gets categorized as IoT, but represents an extreme form of edge computing.
What started with MPUs and other embedded processors has expanded to include composite SoCs in industrial and medical equipment, automobile control systems, entertainment devices and video cameras, DPUs (NVIDIA) and IPUs (Intel) in network interfaces and computational storage drives (CSD) that combine NAND flash, DRAM, processor cores and a PCIe switch in a single component. Each of these operates on data at the source to produce some actionable output and eliminates data movement to a traditional processor.
Although DPUs, CSDs and GPUs are initially being deployed in hyperscale cloud data centers to improve scalability and accelerate computationally intensive algorithms like encryption and deep learning, they can also be used in edge scenarios where the cost and latency associated with moving large amounts of data from thousands of endpoints, whether these are video cameras, self-service retail and point-of-sale systems, equipment sensors or industrial robots is excessive. However, none of these examples resembles the prevailing edge scenario of distributed servers running a VM or container stack in a remote location.
Thus, edge computing encompasses far more than hybrid systems like HCI products, AWS Outposts or Azure Stack Edge running traditional enterprise software, but includes small form-factor (SFF) systems like NVIDIA’s Jetson, sensors embedded with a Raspberry Pi or system components such as a SmartNIC or CFD.
