As both data and processing power rise on the edge of the network, monitoring the performance of edge devices becomes increasingly important. In addition to deploying the AppDynamics IoT monitoring platform to monitor C/C++ and Java apps, end-to-end visibility can be extended to applications running in an AWS Greengrass core by using AppDynamics IoT RESTFul APIs. The easiest way to do this today is with a Lambda function. We recently demonstrated this at AWS re:Invent using Cisco IOx and Cisco Kinetic together with AWS Greengrass on a Cisco Industrial Integrated Services router.
The best thing about this approach is that it opens up a new ecosystem of edge applications to the benefits of unified application monitoring. It ensures customers will resolve incidents faster, reduce downtime, and lower operations’ costs. Meanwhile, the combined strengths of AWS Greengrass and AppDynamics’ IoT Monitoring Platform allow very large volumes of data generated by the Internet of Things to be mined for business insights and harnessed to achieve business objectives.
AWS Greengrass is designed to simplify the implementation of local processing on edge devices. A software runtime, it lets companies execute compute, messaging, data caching, sync, and machine learning (ML) inference instructions even when connectivity to the cloud is temporarily unavailable. Since its release, it has helped accelerate adoption of IoT by making it easier for developers to create and test applications in the cloud using their programming language of choice and then deploy the apps to the edge.
Once the apps are deployed, AppDynamics’ IoT Monitoring Platform provides deep visibility, in real-time, by letting developers capture application performance data, errors and exceptions, and business data. Since the AppDynamics solution is designed for flexible integration at the edge, Lambda functions can be individually instrumented, or a dedicated Lambda function can be written to provide insight into all the Lambdas running. This allows for a wide range of edge applications to monitor any key metric that makes sense to the business.
In the demo at AWS re:Invent, we instrumented an edge application running on a manufacturing floor that was reading sensor data from a programmable logic controller (PLC) over a Modbus interface and reporting it back to the cloud. A key success metric was how edge computing reduced the large amount of inbound data volume to a much smaller meaningful volume that was being pushed to the cloud. AppDynamics provided real-time verification by keeping track of the volume of data being ingested into the Lambda functions, and of the data that was being processed and being sent to the various cloud applications, including AWS Cloud.