Making Sense of the Edge Computing Hardware Landscape

There are so many categories of components (and actual numbers of components) at the edge that most IT shops don’t even know what or how many devices are connected. In a poll conducted last year, ZK Research found that 61% of 841 IT professionals in North America said they had poor or low awareness of which IoT devices are connected.  

“At least with a good edge strategy you’ll close the gap” on the number of connected devices, said Zeus Kerravala, an analyst at ZK Research.

Most edge deployments are “highly custom in nature because of the lack of standard approaches,” said IDC’s Ashish Nadkami in a report on best practices for planning edge infrastructure.

Kerravala advised IT pros to start by planning for the four pillars of infrastructure: storage, security, processing and networking.  “You want some kind of converged platform [with all four pillars], since you can’t buy all these pieces separately,” he said.

“I wouldn’t go fully white box, because that puts a lot of onus on the IT department,” Kerravala added. “I’d look for a turnkey platform with a lot of software flexibility on top. Try to do as much as you can in software.”  

On the other hand, Nadkami advised engineers when picking edge hardware to “stay away from custom hardware.” Instead, IT managers should customize industry-standard hardware, perhaps choosing Raspberry Pi to leverage off-the-shelf Linux or Windows or another OS. With the trend to connect the IT world to the OT (operations technology) world, the use of industry-standard hardware can be further defined in software.

Any hardware approach can involve months of research, lab-testing and field-testing. “There’s definitely a learning curve with edge computing,” Kerravala added. “It’s definitely a different model. It’s still early days of having IT own IoT. I still get a lot of clients just asking what edge is.”

Arpit Joshipura, general manager at the Linux Foundation, added, “If you thought cloud was hard, edge is a thousand times harder. That’s because edge devices are in the thousands and the scale at which you solve the problem is different.”

Edge Case Studies Emerging

Even amid such difficulty, some edge computing case studies are beginning to emerge to show its potential benefits. They offer hints at ways to assemble infrastructure.  

In 2017, Japanese industrial electronics company Daihen Corp. began deploying environmental sensors to monitor dust, moisture and temperature changes during product assembly at a facility in Osaka. Coupled with RFID technology to track each product, the company relied on edge-intelligence software from startup FogHorn Systems to match each product under assembly with environmental conditions detected in various phases and locations of assembly.

The result: a drastic reduction in manual data entry of 1,800 hours a year, and more accurate measurements to satisfy regulators.  The expansion of the system to other factories throughout Japan should be completed this year, resulting in a savings of 5,000 man-hours per year.

FogHorn’s Lightning ML software relies on machine learning to detect anomalies. The data is managed at the edge, which means only the data of greatest value is evaluated, which prevents sending great volumes of data to back-end software.