Are there any specific plugins or data sources recommended for monitoring edge computing applications in Grafana?

Hey everyone,

I’m currently working on monitoring edge computing applications in Grafana and I was wondering if anyone could recommend specific plugins or data sources that would be useful for this purpose. I want to ensure that I have the right tools to collect and monitor data from my edge devices effectively.

I came across a few resources during my search, but I would love to hear from the community if you have any firsthand experience or recommendations. Here are some of the resources I found:
AWS IoT SiteWise Edge and Grafana: This plugin allows you to use Grafana dashboards to monitor industrial data stored by AWS IoT SiteWise in the AWS Cloud.

Grafana Plugins: Grafana offers a variety of plugins that extend its functionality. Data source plugins can communicate with external sources of data and return the data in a format that Grafana understands. App plugins can provide a custom monitoring experience.

Monitoring at the Edge with MicroK8s: This blog post demonstrates how to deploy monitoring tools at the edge using Kubernetes. It might be helpful if you’re using Kubernetes in your edge computing setup.

Azion Grafana Plugin: Azion offers a Grafana plugin that provides a holistic view of your infrastructure at the edge, allowing you to monitor real-time metrics.
I hope these resources can be helpful to others as well. If you have any other recommendations or insights on monitoring edge computing applications in Grafana, please feel free to share them. I’m eager to learn from your experiences.

Thanks in advance for your help!

Hi @adamsmithad ,

If your Lenovo server dan install node_exporter, you can add the node_exporter to the edge computing server.
If your grafana can ping directly to the edge computing server, you can use balckbox _exporter, either use icmp or even http ping for your specific port.
I use this approach for my 200+ IoMT devices, whole over country and I can monitor them real time.