Making Sense of the Edge Computing Hardware Landscape

FogHorn’s solution is hardware agnostic, which means it supports everything from Raspberry Pi to various gateways to industrial PCs. As a result, “there’s no rip and replace” of existing hardware, added Keith Higgins, vice president of marketing at FogHorn.

FogHorn is also providing software for an unidentified oil refinery outside the U.S.  that uses edge computing to analyze the flare, or fire, that comes from a chimney to raise alarms if there is too much smoke or other anomalies that can indicate a problem with a compressor or other equipment.  The approach relies on a ruggedized camera that streams video to a processor that has been trained to recognize unusual characteristics in the flare.

In another example, Schindler Elevator is relying on FogHorn software running on Raspberry Pi devices on top of elevators to connect to existing motion sensors for predictive insights for maintenance.

At Dell, the edge hardware approach is to embed sensors, much of it on Raspberry Pi.

“A lot of people are experimenting in edge computing, even with ‘Pi in the sky’ to connect to the cloud,” said Jason Shepherd, Dell chief technology officer for IoT and edge computing.

He advised IT shops to pick “credible hardware for the environment, with long-term support.” “Credible” could mean rugged — to deal with environmental demands such as tropical weather or high altitude/low pressure — and large enough for application and sensor growth in the next few years. Dell tries to address a highly fragmented market, which means that Dell develops edge hardware and software that includes a small set of SKUs with more features than a customer initially needs, leaving the option to expand as needed later on, Shepherd explained.  

By comparison, at HPE, the philosophy about edge hardware puts a preference on creating a ruggedized data center near where data is created at the edge.  

“There are edge systems with the similar capabilities of a laptop or desktop, but much more is needed, such as a full data center that’s ruggedized for compute, storage and networking capability,” said Tripp Partain, chief technology officer for edge and related fields at HPE. “Raspberry Pi is limited in processing at the edge…As soon as they start putting capability at the edge, people realize they need more compute power.”

With the equivalent of a full data center embedded with OT into a dedicated edge hardware platform, “you have greatly reduced complexity and near real-time decisions,” Partain added.  HPE has relied on its edge computing platform to analyze high definition video in its own manufacturing of servers, improving quality and cutting down the time needed for quality assurance tasks.

HPE’s machine learning edge system is also being used by storage vendor Seagate for analyzing data from electron microscopes used to scan silicon wafers for defects.

As Dell and HPE demonstrate, there are various hardware approaches for edge computing.

MachNation Analyst Josh Taubenheim suggested organizations ask three questions to help decide when to adopt edge computing.  The answers can also help dictate the resiliency and suitability of hardware:

• Is the edge process or device being monitored mission critical?
• Would the IoT solution suffer from loss of connectivity to the cloud?
• Are there regulatory requirements that mandate data be stored locally?

A “yes” answer to any of the above would dictate creating an edge deployment. The answers could also help determine the power, size and complexity of the hardware.