Computing on the Edge—What you Need to Know

While computing via the cloud is clearly a dominant trend today, a concept called edge computing is also growing in popularity, as a supplement to cloud-based data processing. Edge computing is a local strategy where data processing is done near the point where the data is first acquired. For example, a self-driving car generates vast amounts of data constantly. Sending that data to the cloud to be processed would create small delays in analyzing road conditions and safety threats. Such delays, even if tiny, could imperil the car and its occupants.

In contrast, computing at the “edge” of the in-car LIDAR and other devices would be instantaneous, and portions of the data could still be transmitted via wireless networks to the cloud for further analysis. (Recent analysis shows that autonomous vehicles could generate as much as 25 gigabytes of data hourly.)

Many other trends will drive the use of edge computing forward. M2M (machine-to-machine) communications is rapidly emerging in the form of IoT (the Internet of Things). Here, remote sensors gather vast amounts of data from machinery, aircraft, trucks, ships, infrastructure and other vital components of day-to-day life. The intent is to capture data that can be rapidly analyzed, in order to optimize both safety and operating efficiencies. Here, too, sending the data to the cloud may not be fast or efficient enough. For example, edge computing of data continuously gathered from industrial machinery, and later sending the resulting analysis on to a cloud computing center, may save factories from breakdowns and significantly reduce maintenance costs.


For the latest in computing, hardware and software trends analysis and statistics, see: Plunkett's InfoTech Industry Almanac 2018  | InfoTech Industry Overview, and Plunkett Research Online

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