Harnessing the Power of the Intelligent Edge
Over the past 10 years we’ve watched cloud computing come of age as more and more companies send their data to the cloud for processing, storage, and management rather than keeping that data on a local server or Edge gateway. The benefits of cloud computing are vast, however there is another key development on the horizon as the Internet of Things matures: Edge computing.
Edge computing takes some of the essential data processing and analytics work from the cloud and brings it to the Edge devices – not replacing cloud computing, but complementing the solution. Gartner predicts that while currently only 10 percent of enterprise-generated data is processed at the Edge, by 2022 that figure will reach 50 percent. Half of all processing power is expected to slowly shift from the cloud to Edge devices, leading to IoT projects that utilize the power of both cloud computing and the intelligent Edge to make smart business decisions.
There are several trends that point to the need for harnessing the power of the intelligent Edge. First, the onset of Industry 4.0, or the “smart factory,” brought about the fundamental need in the industrial market for an IT/OT converged ecosystem. Project success is evolving to be more than just collecting vast amounts of data, and instead focuses on analyzing the data and creating value. As shown in Figure 1, Industry 4.0 has created new problems for how to manage disparate devices and legacy systems, implementing compliance and standards, security and test practices.
Artificial intelligence, or machine learning, is another key technology driver for Edge computing. Machine learning optimizes performance by quickly processing large, interconnected data sets. Depending on the application, the system is much more efficient at the Edge rather than in the cloud. For instance, consider an AI-enabled manufacturing robot that moves parts across the factory floor. If machine learning happened in the cloud, processing delays may cause safety or performance issues. Also, these manufacturing robots generate tremendous amounts of data, which would make it cost prohibitive to constantly send the data to the Cloud. In order to allow the autonomous vehicle to make decisions on the fly, the data processing must happen at the Edge.
Edge computing use cases range from the Nest to simple manufacturing projects to larger smart city or extended macro-level projects. The fundamental goal for all of these Edge computing projects is to collect data from a large amount of industrial assets, and then put that data to use immediately. The assets may be robotic systems, controllers, PLCs, or many other devices. Some of these devices are prepared for IoT, while others were never designed for IoT projects. The common thread is the challenge of gathering data from those disparate devices, then processing, analyzing and acting on the data right at the Edge of the network or device.
While collecting data used to be the number one concern 10 or 15 years ago, now with the introduction of the cloud and what you can do with that data as soon as you collect it is the priority. Once data is collected from various types of systems and devices in the field, the data must be pushed to multiple applications that can process and analyze the data both at the Edge and in the Cloud. The easiest way to take the next steps is with an Edge computing platform that can discover all of the devices on the network, identify them, and start collecting data within a few minutes.
