Cloud adoption has skyrocketed as businesses seek computing and storage resources that can be scaled up or down in response to changing business needs. But even given the cost and agility advantages of the cloud, there is growing interest in another deployment model: edge computing, which is computation that is performed at or near the source of the data. It can power new use cases, especially the innovative AI and machine learning applications that are critical to modern business success.
The promise of the edge boils down to data, according to three industrial technologists who spoke at the recent future computing conference Hosted by MIT Technology Review. Specifically, there is a need to collect, process, and analyze data closer to where it is generated, whether on the factory floor, in an autonomous vehicle, or in a smart building system.
The ability to run AI models directly on data at the edge without the additional step of moving workloads to the cloud reduces latency and costs. Most importantly, it is the key to unlocking the real-time insights that separate the leaders from the laggards, the panelists agreed.
Enterprises are beginning to recognize the role that 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 of the data center and cloud in 2018, this figure will reach 75% by 2025.
George Small, CTO of moog Inc., a $3 billion motion control solutions company, said it has seen measurable progress from edge applications.
“There are real use cases. Now we are looking at where value is being created,” he said. “It’s actually making significant improvements in…productivity.”
Where the edge meets the cloud
As companies move forward with data-driven business, they need to create an IT landscape that includes cloud and edge 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 steer 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 deeper analysis that can drive smarter decision-making and future business strategy.
Gartner estimates that 10% of enterprise data was created and processed outside of the data center and cloud in 2018.
“Connectivity has gotten to the point where it’s a baseline, which is fueling this idea of a smart edge,” Small said. “Intelligence starts at a detection level at the edge and extends to a networked system of systems that ultimately reaches the cloud. We see it as a continuum.”
Applications where the edge makes the difference
Moog is experimenting with edge computing for a variety of applications, Small said. In the agricultural space, the company is using cutting-edge capabilities and machine learning recognition for almond and apple farming, helping harvest crews autonomously navigate terrain and improve crop yields. In construction, Moog’s AI-based, cutting-edge automation efforts focus on moving materials, for example, turning a part from an excavator into a robotic platform to enable automation, Small said.
Continuing labor and productivity challenges led Moog to experiment with edge-based automation in the agricultural sector, Small said.
“There are opportunities where you don’t have as structured an environment or people need to interact with the actual workplace,” he said. “That was our introduction to this definition of advantage. We approach it from the point of view of the automation of a vehicle”.
Another potential use case combines edge computing, 3D printing, and blockchain to orchestrate on-demand, on-site production of spare parts. Moog customers in sectors such as aerospace and defense could create replacement parts for critical equipment on-site, using blockchain as a means of verifying 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 Sustainable Building Technologies. Smart edge sensors monitor temperature, humidity, and CO2 levels, helping to create a smart building system that can automatically adjust lighting and energy use to keep costs down while optimizing carbon neutrality and maintaining the comfort of the building.
Connecting heating, cooling, and air-filtering systems to peripheral devices creates a smart network that makes it easy to share data and make smarter decisions closer to where they have the most impact.
“You’re building a system of systems and to do the right calculation, you need to have a common network where data can be shared and edge-level decisions can be made” in a matter of milliseconds, Sharma said.
Best Practices for Edge Deployments
Panelists outlined some best practices that can help companies identify the right candidates for edge deployments while avoiding some of the most common deployment challenges.
Move the 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. However, the truth is that the models are complementary, not competitive.
“The rule of thumb is that it’s much better to move computation to data than vice versa,” he said. Roberto Blumoffe, executive vice president and chief technology officer Akamai. “By doing so, you avoid return transportation, which hurts performance and is expensive.”
Consider an eCommerce 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 calculation where that data is stored, in a cloud data warehouse or data lake,” Blumofe said. The perimeter, on the other hand, lends itself to computing data that is in motion, for example analyzing traffic flow to initiate security action.
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. However, capabilities are rapidly improving and companies cannot afford to remain on the sidelines.
“You really need to start pushing because value can be created,” Small said. “You have to be out there looking for new opportunities, you’re not just going to think about them, you have to find them.”
Don’t skip ROI. Edge-enabled automation can help companies do more with less labor and free up people to do more value-added work, Moog’s Small said. But in addition to those obvious blue-chip productivity gains, there are other, more difficult-to-quantify benefits of automation at the edge, including repeatability, he said.