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Industrial automation professionals are no strangers to distributed control and execution environments. Process automation engineers have long relied on Distributed Control Systems (DCSs) for control of numerous distinct process control loops, while Programmable Logic Controllers (PLCs) are the primary workhorses for discrete on/off control of numerous input/output devices in applications such as automated machinery. Not surprisingly, this familiarity leads to some confusion when the term “edge computing” is raised in an IIoT context. Been there, been doing that for decades.
One way to distinguish traditional distributed industrial automation from the IIoT-driven trend toward edge computing is to focus on exactly what is being executed where. In the control environment, the emphasis is on local process and logic control programs that execute on the basis of numerous inputs from, and outputs to, field automation equipment. Edge computing, on the other hand, typically entails local or distributed execution of applications traditionally associated with higher levels of the architecture, particularly cloud-based applications, and not execution of local control logic.
Edge analytics, one of the earliest “killer apps” in edge computing, is a perfect example of this difference. Cloud-based analytics applications have been available for some time, but with the advent of the IIoT some of this functionality is migrating out of the cloud and onto the network edge. This is particularly true in the important realm of predictive analytics that help reduce downtime, maximize performance, enhance production operations, and deliver other important IIoT business value propositions. These edge computing apps provide important feedback to the control process to these ends, but typically do not execute the control logic or algorithms themselves. Availability of analytical feedback on or near the target assets delivers speedy (ultimately even real-time) feedback.
This emphasis on bringing higher-level, typically cloud-based functionality to the edge, as well as support of device-to-cloud integration, are primary differentiators between edge computing and today’s control and HMI environment. Edge or fog computing strategies rely on microprocessor-based devices with standard operating systems capable of hosting applications that can be executed at the edge. How much edge processing to move to network edge devices is still under discussion and varies by customer profile.
Content for this blog was drawn from in part from ARC’s recently-released Industrial Gateways Global Market Research Study, part of the company’s ongoing coverage of the markets for Industrial Internet of Things devices, network infrastructure, and enterprise applications. Further information is available at:
/market-studies/industrial-network-gateways
One way to distinguish traditional distributed industrial automation from the IIoT-driven trend toward edge computing is to focus on exactly what is being executed where. In the control environment, the emphasis is on local process and logic control programs that execute on the basis of numerous inputs from, and outputs to, field automation equipment. Edge computing, on the other hand, typically entails local or distributed execution of applications traditionally associated with higher levels of the architecture, particularly cloud-based applications, and not execution of local control logic.
Edge analytics, one of the earliest “killer apps” in edge computing, is a perfect example of this difference. Cloud-based analytics applications have been available for some time, but with the advent of the IIoT some of this functionality is migrating out of the cloud and onto the network edge. This is particularly true in the important realm of predictive analytics that help reduce downtime, maximize performance, enhance production operations, and deliver other important IIoT business value propositions. These edge computing apps provide important feedback to the control process to these ends, but typically do not execute the control logic or algorithms themselves. Availability of analytical feedback on or near the target assets delivers speedy (ultimately even real-time) feedback.
This emphasis on bringing higher-level, typically cloud-based functionality to the edge, as well as support of device-to-cloud integration, are primary differentiators between edge computing and today’s control and HMI environment. Edge or fog computing strategies rely on microprocessor-based devices with standard operating systems capable of hosting applications that can be executed at the edge. How much edge processing to move to network edge devices is still under discussion and varies by customer profile.
Content for this blog was drawn from in part from ARC’s recently-released Industrial Gateways Global Market Research Study, part of the company’s ongoing coverage of the markets for Industrial Internet of Things devices, network infrastructure, and enterprise applications. Further information is available at:
/market-studies/industrial-network-gateways