Processing data locally isn’t new. That’s pretty much how things were done before widespread network connectivity. Edge computing is a matter of meshing the local and the cloud appropriately through a gateway, distributing data storage and analytics in the optimal way, while ensuring that result is seamless to the user.
Choosing between edge and cloud
If data transmission were free and took no time to get to and from the cloud, then it would make sense to process everything centrally, in a giant Google, Rackspace, or AWS server farm. It’s cheap, easy to scale, and ensures that data is always available for various levels of analytics.
But data transmission’s costs rise with data volume and distance, and transmit to receive time (usually called latency) can become significant for time-critical operations. The importance of edge processing grows as that edge gets farther away and harder to access.
Two main considerations govern edge computing choices:
So edge capability becomes relatively more important as the location grows remote, the production of data greater, and the need for quick response more critical. As a result, many of the most active edge computing efforts are taking place in various parts of the energy industry, with its remote production facilities and wide-flung transmission networks, complex processes, and minimal-downtime requirements.
Oil and gas
An offshore oil rig generates over 1TB of data per day, much of it related to time-sensitive functions and equipment status. Transmitting that mass of data, processing it, and returning instructions can take days. A malfunction can cost hundreds of thousands of dollars per day in lost production. Since each device often has its own diagnostics, while the platform as a whole has significant computing resources, along with trained staff, it makes sense to keep time-sensitive decisions on-site, while sending cleaned and processed data to the cloud where it can be used to make larger-scale production and utilization decisions.
Remotely located wind turbines need to respond individually on a second-by-second basis to changes in the wind and other conditions to maximize efficiency, while the wind farm as a whole needs to integrate the changes in each turbine to make larger-scale decisions on a slightly longer time horizon. Again, edge processing can then forward actionable data to a central location for higher-level business decisions.
Self-driving cars are on the way. These will be perhaps the most visible manifestation of edge computing and analytics for the IoT. Each vehicle needs to make instant-by-instant decisions that affect human safety as well as longer-term decisions about their engines and other systems, while also providing traffic control and other systems with the appropriate granularity of data.
Keep your eyes on the edge(s)
There is more than one edge, and more than one way to balance those edges against the center. Look for a wide variety of implementations involving both familiar and new industry players, each with a specific take.