By now, you’re familiar with The Edge. For those who have been living under a rock over the past five years, The Verge defines edge computing as a geographically distributed processing paradigm. While this is a somewhat simplified look at what edge computing is, it’s technically correct. Edge computing allows complex systems to divest their processing to nodes, allowing for faster updates to systems that are further away from the central server. Each node can then serve as a mini-server, doing processing on its own and shunting results back to the central server as needed.
Automated processing on nodes makes for a much smarter and well-designed network. One of the most popular recent uses of edge computing is in IoT networks. These networks utilize small, low-power devices to process data far from the central server. If the devices run into an error, they may send the data they failed to process to the central hub for consideration. IoT devices already exist in their own local area network, usually defined via their built-in connectivity. Unfortunately, these devices have significant shortfalls, the inability to deliver adaptable solutions being the most significant one. But what if you combined the edge with artificial intelligence?
Connectivity has increased since the early days of the internet. High-speed connections are now the norm, seeing consumers stream more data over a single link that they could have dreamed a mere decade ago. This increase in bandwidth is a significant step towards realizing a smart connected edge. The edge reduces processing latency. This division of labor can be a boon in massive networks, sometimes speeding up response times by two or three times. With smaller, connected networks, there’s also less rubbish traffic and a lower chance of routing errors. What’s more, with this amount of data, it’s easy to see how streaming AI can fit into the picture.
Understanding Enterprise Edge Computing
Some companies have already started to salivate at the thought of edge computing replacing hyper-scale cloud processing, but this sort of thinking is ill-informed. Instead of replacing hyper-scale cloud computing, edge computing will assist it to be more efficient. The cloud servers already have a robust processing framework and can do any heavy lifting if required. Edge processors can take up the slack and help the central hub deal with routine tasks, only sending the complicated ones back for specialized processing. To achieve this goal of aiding cloud computing centers to meet their goals, the intelligent edge must display the following characteristics:
o Hardware: Currently, edge devices are small, agile, and extremely energy efficient. They can run in multiple configurations, ranging from a routing hub to a small micro-processing center. Modern technology has increased functionality for edge devices allowing them to offer services like virtualization, telecoms, and automation.
o Advanced Connectivity Solutions: Wi-fi, 4G LTE, and recently 5G have allowed for faster, more secure connections between devices. Currently, 5G networks offer millisecond-level latency and massive bandwidth, allowing devices to connect and transfer data packets faster than ever before. Local area networks formed from these devices can provide a lot for businesses.
o Artificial Intelligence: Intelligent edge requires a substantial helping of AI. Many of the chips embedded in these devices can perform complex machine learning based on desired outcomes, adjusting with each iteration to be better and more efficient.
Application of Intelligent Edge in The Real World
It’s fun to talk about this system’s theoretical application, but how can we turn tech into reality? There are already a few businesses and government agencies exploring intelligent edge as a solution to their problems. Medical firms have developed a combination of AI and edge computing to allow cameras to scan patients for diabetic eye disease. NVidia informs us that these devices use an onboard, scaled-down supercomputer alongside machine learning algorithms to make their determinations.
Another classic example of intelligent edge providing solutions to real-world problems is in India, helping the country deal with food production. Hewlett Packard mentions that through a sophisticated network of sensors for rainfall, moisture, growth, and several other metrics, they’re helping farmers ensure a harvest each year. The company hopes their input can help to provide food for the world’s growing population.
Not Omniscient, But Powerful
While the system isn’t able to tell how to bet on Super Bowl 2021, it still gives you a lot of fascinating insight into how technology will evolve. Machine learning can turn the edge into a supercomputer, making hyper-scale cloud computing even more potent through distribution. We’ve already seen how much real-world benefit the technology can bring to people around the world. The only thing we have to consider is how it will affect us in the future. It may be some time before the intelligent edge becomes a mainstream concern. However, it may be sooner than we think that it becomes as ubiquitous as social media is to users today.
