What’s Behind AT&T’s Big Bet on Edge Computing
Hyper-scale cloud has big advantages in scale and efficiency, but for some things you need to have the computation done closer to the problem. That’s what AT&T is promising with its upcoming edge computing services that will put micro data centers in its central offices (think telephone exchange), cell towers, and small cells.
Eventually this edge computing network will use the future 5G standard to lower the latency even further. That will open up possibilities like using high-end GPUs AT&T says it will place at the edge of its network for highly parallel, near-real-time workloads. Take off-board rendering for augmented reality for example. Instead of rendering the overlay for AR frame by frame on your device, a cloud system that doesn’t have to worry about using up too much battery power could pre-render an entire scene and then quickly send what’s relevant as you turn your head.
“Today, one of the biggest challenges for phones running high-end VR applications is extremely short battery life due to the intense processing requirements,” an AT&T spokesperson told us. “We think this technology could play a huge role in multiple applications, from autonomous cars, to AR/VR, to robotic manufacturing and more. And we’ll use our software-defined network to manage it all, giving us a significant advantage over competitors.
The definition of edge computing is a little fuzzy; often it refers to aggregation points like gateways or hyperconverged micro data centers on premises, and what AT&T is promising from its tens of thousands of sites “usually never farther than a few miles from our customers” is perhaps closer to fog computing.
“Edge is different things to different people. Every vendor defines the edge as where they stop making products, and for AT&T the edge of their network is the RAN (Radio Access Network),” Christian Renaud, IoT Research Director at 451 Research, told Data Center Knowledge. “They’re talking about multi-access edge computing, MEC, which is a component of fog computing. For AT&T, it’s their way of saying ‘don’t just treat us as backhaul, or as a dumb pipe to hyper-scale cloud’.”
New categories of applications — from data analytics using information from industrial sensors to upcoming consumer devices like VR headsets — are pushing the demand for compute that’s closer to where data is produced or consumed. “This is because of applications like autonomous vehicles co-ordination — vehicle to vehicle and vehicle to infrastructure — or VR, where because of the demands of your vestibulo-ocular reflex for collaborative VR, there are fixed latencies you have to adhere to,” he explains. In other words, a VR headset has to render images quickly enough to trick the mechanism in your brain responsible for moving your eyes to adjust to your head movements.
“There are applications that demand sub-10 millisecond latency, and there’s nothing you can do to beat the speed of light and make data centers respond in five or 10 milliseconds,” Renaud said. “It’s impossible to haul all the petabytes of data off the sensors in a jet engine at the gate to the cloud and get the analysis that says the engine is OK for another flight in a 30-minute turnaround time.”
See also: Edge Data Centers in the Self-Driving-Car Future
Physics dictates that the compute-analysis-action loop occurs closer. It needs to happen in milliseconds, preferably single-digit or low-double-digit milliseconds, and that dictates geographical placement of edge computing capacity. It can take the shape of onboard compute on the device itself, a dusty old PC on the manufacturing floor, or a server in a colocation data center. Data from non-stationary devices has to go to the MEC (via a RAN) as its first stop, and there’s lots of opportunity for network operators to add value beyond transport by pushing compute closer to the edge.
That’s what AT&T is betting on.
