Cloud vs. Edge – as previously published in IoT Agenda
For many industrial automation professionals, the cloud has come to represent the backbone of the IIoT. But, for enterprises to really make progress with their IIoT visions, they must begin to realize that the cloud is only one part of their IIoT universe. Operations that need their computing done in real time are discovering that there are certain things that cannot or should not be pushed to the cloud — whether it be for security, latency or cost concerns — and are therefore beginning to push more and more computing to the edge of their networks.
The growth in edge computing has not only created more data, but also a greater need for speed in making that information available for other systems and analytics. Cloud computing is convenient, but its connectivity often just isn’t robust enough for certain industrial situations. Some computing will always need to live at the edge, such as real-time processing, decision support, SCADA functions and more. There’s no sense in limiting these functions when 100% cloud adoption just isn’t necessary and can instead be utilized for non-real-time workloads like post-processing analytics or planning.
Cloud vs. Edge: A Real-World Example
Consider an example from the energy industry that demonstrates cloud vs. edge, with each playing their most appropriate role. Companies can have hundreds of oil drilling rigs dotted across a region, with the company headquarters where the data center or cloud resides being hundreds or even thousands of miles away. At each of the oil rigs, or the edge, it’s necessary to have systems that provide continuous monitoring and analysis of key parameters — like well pressure levels — with the ability to identify when critical thresholds are at risk of being exceeded, allowing operators to take immediate action to mitigate them. It could pose an unreasonable risk to wait for this data to travel back to the data center, undergo analysis and direct actions back to the rig.
In this instance, the cloud would be better suited to support planning and trend-spotting by collecting metrics from all of the oil rigs and periodically sending them to the data center or cloud where they can be aggregated and analyzed.
