Navigating IIoT Protocols: Comparing DDS and MQTT
The convergence of Operational Technology (OT) and Informational Technology (IT) has become a strategic imperative for organizations aiming to unlock...
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RTI is the largest software framework company for autonomous systems. The company’s RTI Connext product enables intelligent architecture by sharing information in real time, making large applications work together as one.
How do you ensure that your IIoT system is healthy? When your system is running, it may experience network loss or delay, node failures, or unexpected changes due to software upgrades and new application deployments. These problems affect the performance of your application. But if you do not continuously monitor them, identifying the source of the problem can be quite challenging. The RTI Research team is working on architectural solutions for operational monitoring of distributed energy systems. However, this approach can be applied to any vertical application, including yours.
Operational monitoring provides you with a clear understanding of your system health by collecting performance metrics and events over time. Specifically, it gives you insights through real-time visualization and analysis. To support this operational monitoring capability for DDS-based systems, the RTI Research team evaluated relevant technologies and developed prototype software for demonstration (this work was done as part of a DOE-funded research contract).
Three key components are needed for monitoring: a solution for data collection, a solution for data storage and a solution for visualization.
Time-Series Database for Operational Monitoring
For operational monitoring, we used a software stack from InfluxData called TICK (derived from the initials of each technology). It is shown in the figure below. Telegraf is a plugin-driven agent for collecting monitoring data. It supports more than 100+ plugins so you can collect data from many different sources. You can also extend your monitoring sources by developing your own plugin. Once monitoring data is collected by Telegraf, collected data is handed off to InfluxDB -- a data time-series monitoring technology. From InfluxDB the data can be passed on to Chronograf for visualization; Kapacitor provides alerting based upon user-defined rules.
In particular, InfluxDB is an open source time-series database for monitoring that provides several interesting features:
Monitoring Architecture and Implementation
TICK formed the foundation of our Administration Layer (as depicted below). In addition, we needed to provide tools that generated the health monitoring data – what we call our Management Services Layer.
The figure above describes the monitoring architecture that we built for our project. This architecture consists mainly of a management services layer and administration layer.
The types of data we collected with this architecture include:
To implement the architecture, we used existing Telegraf plugins to collect node and container metrics. These metrics are collected from an operating system and a container engine. For DDS metrics, we leveraged RTI Monitoring Library.
Our intelligent bridge transforms locally-collected data from our monitoring agents into remote data to be passed over the monitoring databus. The bridge can filter the collected data to reduce data over network and also enrich it (e.g., adding hostname as a tag to group time-series data) if needed.
To subscribe to data from the monitoring databus at the administration side, we used a DDS plugin-enabled Telegraf (Metrics Collection Service in the architecture). As the Telegraf plugin framework is written in Go, we also developed a DDS Go binding with RTI Connector! It is currently available at https://github.com/rticommunity/rticonnextdds-connector-go. For visualization and alerts, we used Grafana.
With all of these artifacts, we could demonstrate an end-to-end operational monitoring capability for DDS-based systems using our energy system simulations as user applications (available via our Case + Code page: https://www.rti.com/resources/usecases/microgrid-openfmb). We are happy to share our work and get feedback from you. If you are interested, please let us know!
In the next blog we will delve much deeper into our InfluxDB integration and provide you with source code and documentation so that you can give it a spin for yourself!
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