Like other hot new areas of enterprise tech, Edge Computing is a broad architectural concept rather than a specific set of solutions.
Primarily, Edge Computing is applied to low-latency situations where compute power must be close to the action, whether that activity is industrial Internet of Things (IoT) robots flinging widgets or sensors continuously taking the temperature of vaccines in production. The research firm Frost & Sullivan predicts that by 2022, 90 percent of industrial enterprises will employ Edge Computing.
Edge computing is a form of distributed computing that extends beyond the data center mothership.
When you think about it, how else should enterprises invest in the future? Yes, we know that a big chunk of that investment will go to the big public Cloud providers – but hardware and software that enterprises own and operate isn’t going away. So why not physically distribute it where the business needs it most?
Augmenting the operational systems of a company’s business on location – where manufacturing or healthcare or logistical operations reside – using the impressive power of modern servers can deliver all kinds of business value.
Typically, Edge Computing nodes collect lots of data from instrumented operational systems, process it, and send only the results to the mothership, vastly reducing data transmission costs. Embedded in those results are opportunities for process improvement, supply chain optimisation, predictive analytics, and more.
Increasingly, though, the biggest benefit of Edge Computing is the ability to process and store data faster, enabling for more efficient real-time applications that are critical to companies. Before Edge Computing, a smartphone facial recognition scan would need to run the associated algorithm through a Cloud-based service, which would take a lot of time to process. With an Edge Computing model, the algorithm could run locally on an Edge server or gateway, or even on the smartphone itself, given the increasing power of smartphones. Applications such as virtual and augmented reality, self-driving cars, smart cities and even building-automation systems require fast processing and response.
It's clear that while the initial goal for Edge Computing was to reduce bandwidth costs for IoT devices over long distances, the growth of real-time applications that require local processing and storage capabilities will drive the technology forward over the coming years.