For many people, the cloud has come to represent the backbone of the industrial internet of things. But, enterprises really making progress with their IIoT visions are starting to realize that 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.Content Continues Below
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.
A real-world example
Consider an example from the energy industry that demonstrates edge and cloud 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.
Operational technology teams have managed these systems for years. They understand the network edge and what it requires. But there’s a cultural divide between OT and those IT professionals pushing cloud-based IIoT. The IT teams often equate the approach necessary for industrial automation with that of enterprise IT deployments.
But even traditional IT enterprises have figured out that it’s a hybrid cloud world. An industrial automation engineer I spoke with recently told me that 15% of their data generated from the plant floor needs to be sent to the cloud to be immediately available to other systems. What happens with the other 85%? That portion must be aggregated and analyzed to determine how it can be valuable. If it’s all being pushed up to the cloud, however, industrial businesses are now paying for all that capacity when they really only need a fraction of it. That’s a major cost issue.
For those beginning to implement an industrial IoT strategy, what’s important to remember is to not make an investment decision before first carefully evaluating your workloads and flow of information. The cloud is absolutely a necessary piece of IIoT deployments, but that doesn’t mean you should abandon edge computing systems that can continue to keep valuable and mission-critical information safe and quickly attainable. Finding the right balance will help operators find success with IIoT.
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