The promise of edge computing comes down to data | MIT Sloan

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Edge computing is unlocking the real-time insights and intelligent automation that separate business leaders from the laggards.

Cloud adoption has rocketed as companies seek computing and storage resources that can be scaled up and down in response to changing business needs. But even given the cost and agility upsides to cloud, there’s rising interest in yet another deployment model — edge computing, which is computing that’s done at or near the source of the data. It can empower new use cases, especially the innovative artificial intelligence and machine learning applications that are critical to modern business success.

The promise of the edge comes down to data, according to three industrial technologists who spoke at the recent Future Compute conference hosted by MIT Technology Review. Specifically, there is a need to gather, process, and analyze data closest to where it’s being generated, whether that’s on the factory floor, in an autonomous vehicle, or in a smart building system.

The ability to run artificial intelligence models directly on data at the edge without the extra step of moving workloads to the cloud reduces latency and costs. Most important, it is the key to unlocking the real-time insights that separate the leaders from the laggards, the panelists agreed.

Companies are starting to recognize the role edge computing can play in driving successful data-driven business transformation. Gartner estimates that while only 10% of enterprise data was created and processed outside the data center and cloud in 2018, this number will be 75% by 2025.

George Small, the chief technology officer of Moog Inc., a $3 billion motion control solutions company, said he’s seen measurable progress from edge applications.

“There's real use cases. We're now seeing where value's being created,” he said. “It's actually making significant improvements in … productivity.”

As companies move ahead with data-driven business, they need to create an IT landscape that includes both edge and cloud computing. Data collected and analyzed at the edge can initiate a real-time response to troubleshoot a piece of industrial equipment to prevent machinery downtime or to redirect a self-driving car out of harm’s way.

At the same time, device data from that machine or vehicle can be sent to the cloud and aggregated with other data for more in-depth analysis that can drive smarter decision making and future business strategy.

“Connectivity has gotten to the point that it’s a baseline, which is feeding this idea of an intelligent edge,” Small said.“Intelligence starts at a sensing level at the edge and spans to a networked system of systems that ultimately gets to cloud. We look at it as a continuum.”

Moog is experimenting with edge computing for a variety of applications, Small said. In the agricultural space, the company is using edge capabilities and machine learning recognition for almond and apple farming, helping harvesting equipment autonomously navigate terrain and improve crop yields. In construction, Moog’s edge and AI-based automation efforts are focused on material movement — for example, turning a piece of an excavator into a robotic platform to enable automation, Small said.

Ongoing labor and productivity challenges drove Moog to experiment with edge-based automation in the agriculture sector, Small said.

“There are opportunities where you don’t have as much of a structured environment or people need to interact with the actual work site,” he said. “That was our introduction to this definition of edge. We came at it from the point of view of automating a vehicle.”

Another potential use case combines edge computing, 3D printing, and blockchain to orchestrate on-demand, on-location output of spare parts. Moog customers in sectors like aerospace and defense could create spare parts for critical equipment on-site, using blockchain as a means to verify the providence and integrity of the part, Small said.

At Honeywell Building Technologies, edge computing is a key part of transforming building operations to improve quality of life, said Manish Sharma, vice president and general manager of Honeywell’s sustainable building technologies. Smart edge sensors monitor temperature, humidity, and CO2 levels, helping to create an intelligent building system that can automatically adjust energy and lighting use to keep costs down while optimizing for carbon neutrality and maintaining building comfort.

Connecting heating, cooling, and air filtering systems to edge devices creates an intelligent network that facilitates data sharing and makes smarter decisions closer to where they have the most impact.

“You’re building a system of systems and to do the right computation, you need to have a common network where data can be shared and decisions can be made at the edge level” in a matter of milliseconds, Sharma said.

The panelists outlined some best practices that can help companies identify the right candidates for edge deployments while avoiding some of the more common deployment challenges.

Move computing power to where the data is. Determining whether edge or cloud is optimal for a particular workflow or use case can cause analysis paralysis. Yet the truth is the models are complementary, not competing.

“The general rule of thumb is that you’re far better moving compute to the data than vice versa,” said Robert Blumofe, executive vice president and chief technology officer at Akamai. “By doing so, you avoid back hauling, which hurts performance and is expensive.”

Consider an e-commerce application that orchestrates actions like searching a product catalog, making recommendations based on history, or tracking and updating orders.

“It makes sense to do the compute where that data is stored, in a cloud data warehouse or data lake,” Blumofe said. The edge, on the other hand, lends itself to computing on data that’s in motion — analyzing traffic flow to initiate a security action, for example.

Go heavy on experimentation. It’s still early days in edge computing, and most companies are at the beginning of the maturity curve, evaluating how and where the model can have the most impact. Yet capabilities are improving rapidly and companies can’t afford to remain on the sidelines.

“You really need to start pushing because there is value to be created,” Small said. “You have to be out there looking for new opportunities — you’re not just going to think them up, you have to find them.”

Don’t skip over ROI. Edge-enabled automation can help companies do more with less labor and free up people to do higher value-added work, noted Moog’s Small. But in addition to those obvious first-order productivity gains, there are other, harder to quantify benefits from automation at the edge, including repeatability, he said.

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