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The Cloud Comes to You: AT&T to Power Self-Driving Cars, AR/VR

and other Future 5G Applications Through Edge Computing

Powerful Compute Capabilities will Drive Down Latency for Next-Gen Applications

AT&T is reinventing the cloud to

boost the potential of self-driving

cars, augmented reality and virtual

reality, robotic manufacturing, and

more. We’re embracing a model

called edge computing (EC) to

move the data crunching from

the device to the cloud. Driving it

will be the single-digit millisecond

latency that only tomorrow’s 5G

can deliver. And powering it all will

be our software-defined network,

the most advanced of its kind in the

networking industry.

Here’s the challenge: Next-gen

applications like autonomous cars

and augmented reality/virtual

reality (AR/VR) will demand

massive amounts of near-real time

applications will be delivered

through your smartphone. But

creating entirely virtual worlds

or overlaying digital images and

graphics on top of the real world

in a convincing way also requires a

lot of processing power. Even when

phones can deliver that horsepower,

the tradeoff is extremely short

battery life.

Edge

computing

addresses

those obstacles by moving the

computation into the cloud in a way

that feels seamless. It’s like having

a wireless supercomputer follow

you wherever you go.

“Edge computing fulfills the

promise of the cloud to transcend

the physical constraints of our

computation.

For example, according to some

third-party estimates, self-driving

cars will generate as much as 3.6

terabytes of data per hour, due to

the clusters of cameras and other

sensors required to enable their

digital vision. Some functions, such

as braking, turning and acceleration

will likely always be managed by

the computer systems in the cars

themselves. But what if we could

offload to the cloud some of the

secondary systems? These include

things like updating and accessing

the detailed maps these cars will

use to navigate.

Or consider AR/VR. The industry

is moving to a model where those

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