Edge Computing and Cloud Computing: Balance in Action
Let’s look at a couple of illustrations. In an industrial context, examples of intelligent edge machines abound—pumps, motors, sensors, blowout preventers and more benefit from the growing capabilities of edge computing for real-time analytics and actuation.
Take locomotives. These modern 200 ton digital machines carry more than 200 sensors that can pump one billion instructions per second. Today, applications can not only collect data locally and respond to changes on that data, but they can also perform meaningful localized analytics. GE Transportation’s Evolution Series Tier 4 Locomotive uses on-board edge computing to analyze data and apply algorithms for running smarter and more efficiently. This improves operational costs, safety, and uptime.
Sending all that data created by the locomotive to the cloud for processing, analyzing, and actuation isn’t useful, practical, or cost-effective.
Now let’s switch gears (pun intended) and talk about another mode of transportation—trucking. Here’s an example where edge plays an important yet minor role, while cloud assumes a more dominant position. In this example, the company has 1,000 trucks under management. There are sensors on each truck tracking performance of the vehicle such as engine, transmission, electrical, battery, and more.
But in this case, instead of real-time analytics and actuation on the machine (like our locomotive example), the data is being ingested, then stored and forwarded to the cloud where time series data and analytics are used to track performance of vehicle components. The fleet operator then leverages a fleet management solution for scheduled maintenance and cost analysis. This gives him or her insights such as the cost over time per part type, or the median costs over time, etc. The company can use this data to improve uptime of its vehicles, lower repair costs, and improve the safe operation of the vehicle