Edge application orchestration: a critical piece of the IoT puzzle by Vatsal Shah.
As the Internet of Things comes of age, edge computing is taking some of the essential data processing and analytics work from the cloud and bringing it to the intelligent edge.
The true benefit of edge computing is realized for devices that produce vast amounts of data that can be best processed more efficiently at the edge instead of transferring all of the data across a network to the cloud. Large-scale projects of this type may include hundreds or even thousands of IoT devices, and a smarter edge means real-time local data and a plethora of possibilities for business use cases.
What is edge application orchestration management?
There is a lot going on at the intelligent edge – and edge application orchestration management is the logic (or the glue) that defines how the data flows between devices and applications to produce business intelligence. Edge app orchestration management can make deploying and managing an application for one IoT device the same as for a hundred – saving time and money across the life of a project. The ability to manage all deployments from one place and see the application lifecycle with a single view significantly reduces the burden of IT resources while providing a uniform experience across the distributed enterprise.
The benefits of edge app orchestration are becoming even more critical as IoT projects mature. There are many legacy automation systems in the market and enterprises are looking to update those systems by adopting new IoT technology. Edge app orchestration makes it possible to take legacy systems into the new era of the intelligent edge to make use of real-time data.
Bringing legacy automation to the edge
For instance, consider legacy automation systems that are set up to manage the logic to produce an end result, such as a CNC machine. The system has a fixed job to do; the system is not using advanced IoT concepts such as machine learning. However, in order to do something more and expand the system, you can attach an edge device to collect the variables these automation systems are using. By collecting that data in an edge device, normalizing it, and then using it for running applications you can create a more advanced system to predict failure.
Suppose you want to understand why the average temperature is increasing in the CNC machine, and what factors are contributing to the temperature rise. You can build an application to predict failure based on a number of parameters, and then you can push it to all of the IoT devices and machines to determine why the temperature is increasing. You can build one application but it needs to be deployed on 10 different assembly lines.
From coding to building the application, to distributing it and storing data – the whole lifecycle can be managed via edge app orchestration management. An edge computing platform is the framework that makes it possible for multiple applications to use the data in an efficient manner. Some edge computing platforms can also access a marketplace of applications to simplify and speed up the process.
Once the application is created or accessed via the marketplace, it can be distributed to devices simply through the edge computing platform. A good platform will allow you to make sure the applications are installed and working across all devices, getting results and then understand the patterns. Now you can understand why the temperature is increasing, identify a solution, and create a full feedback loop across all of your devices and assets.
Beyond temperature the same process could be followed on the same legacy automation system for something else such as production scheduling or moisture detection. No matter what the problem might be, the same isolated edge devices can use a suite of services via an edge computing platform that allows for edge application orchestration management.