Optimizing your application architecture at the ‘federated edge’
Optimizing applications for the sprawl we call the Internet of Things is a daunting challenge.
To craft high-performance IoT apps, developers need a federated environment that distributes algorithmic capabilities for execution at IoT network endpoints, also known as “edge devices.” Federation is essential because many IoT edge devices — such as mobile phones — lack sufficient local resources for storing all data and executing all the algorithms needed to do their jobs effectively.
Key among the capabilities being federated to the IoT edges are machine learning, deep learning and other cognitive-computing algorithms. These analytic capabilities enable IoT edge devices — such as drones, self-driving cars and industrial robots — to make decisions and take actions autonomously based on locally acquired sensor data. In particular, these algorithms drive the video recognition, motion detection, natural-language processing, clickstream processing and other real-time pattern-sensing applications upon which IoT apps depend.
Federated decisioning, driven by device-embedded ML/DL, is the essence of a well-tuned edge application. As my Wikibon colleague David Floyer recently remarked on theCUBE:
“When you look at the amount of data that is expected to come from video, Wikibon did some research a few years ago looking at the amount of data from different types of sensors, and video is, around 40 percent of all the data is going to be coming in from video…. [T]he interesting new thing is then, where do you make that [sensor-driven] decision? Do you take a snapshot of that head, make a thumbprint, and send it up to the cloud? Do you send it to some other process locally? Or do you keep it very close to the camera? And most of, most people agree that the final landing point for this technology of recognition is going to be in the camera itself. Why? Because that’s where most of the data is. It’s raw data, it’s the original data, you want to make the decision there and then. That puts a time scale on the decision. So you’ve got to put in a fair amount of compute power, into that device. In this case, the camera itself… perhaps also with a real-time streaming feed of decision logic or instructions from some application in the cloud that’s looking at the broader pattern related to patterns that it sees coming in from sensors at other edges.
