The Intelligent Edge: Real Benefits from the Continuum Between Cloud and Things
by Dr. Mung Chiang, John A. Edwardson Dean of the College of Engineering and the Roscoe H. George Professor of Electrical and Computer Engineering, Purdue University
As the IoT expands into every corner of the world, there is no question that intelligent edge architecture is critical to ensure the viability of the enterprise. And not just critical, but unavoidable.
While traditional enterprises have embraced the cloud computing paradigm, significant computing resources still need to be deployed locally to handle tasks that are not suitable for the cloud, or augment the work of the cloud.
A distinguishing characteristic of the IoT is that there will be billions of devices generating exabytes of data, but they will have little computational ability of their own.
There is no feasible architecture that could get that data to a central data center, process it, and get the pertinent results back to the devices in any reasonable amount of time. The network bandwidth requirements would be prohibitive, assuming the capacity existed at all.
But the world of industrial control systems demands latency of real-time or near real-time, certainly no more than a few milliseconds. Consider drone flight control, autonomous vehicle-to-vehicle communications, or smart city emergency systems. These requirements fall well outside what traditional cloud architectures can bring to the table.
At the core of this very real challenge are the decisions about where to compute and where to store data on a substrate of variably available nodes. We can view this as a service continuum between the cloud and the things.
To understand the edge deployment hierarchy, take the simple example of a cell phone providing the intelligent edge for a user’s wearable devices, such as a Fit Bit. When that user is in her car, the car can become the edge for the phone, allowing many functions to be moved to the vehicle.
