In a World Short on Memory, Lean Network Monitoring Still Matters

Today, the tech industry is grappling with a global memory-chip crunch. Demand for DRAM and NAND is being driven to unprecedented levels by AI infrastructure build-outs, leading to supply tightening and soaring prices.

For many organizations, this shortage has real cost and procurement implications. RAM and flash memory have suddenly become expensive and hard to source.

In that environment, deploying a heavy, resource-hungry monitoring stack isn’t just difficult; it could be impossible. That’s where the virtues of a lean network monitoring system like OpenNMS come into play.

OpenNMS can run effectively in memory-constrained environments. That’s not just our opinion; we’re backing it up with fully open benchmarking that anyone can examine themselves.

As an example, we have been testing OpenNMS performance when OpenNMS Core is used to collect metrics and then forward them to a northbound system via Kafka, and the results speak for themselves. With an OpenNMS core system that has only 8GB of memory allocated for the Java Heap, we can push well over 3600 metrics per second (or 176 metrics per host with over 6000 hosts with our default 5-minute interval collection).

Furthermore, the design of the OpenNMS architecture is flexible, enabling you to put resources where you need them, be that on the OpenNMS Core, the remote collecting Minions, or downstream on your backend databases for metric storage.

Java has a reputation for being a resource hog, and it couldn’t be further from the truth. The fact is that well-architected and well-built Java enterprise applications can be some of the most performant applications in an IT environment. You don’t just have to take our word for it, as one of our long-time community members recently put it: “I used to believe that OpenNMS was a resource hog; I no longer believe so.”

The benchmarks also cover event-driven workloads (like SNMP traps) and high-volume log ingestion (syslog). The “SNMP Traps load testing” and “Syslog load testing” scenarios demonstrate that OpenNMS doesn’t require heavy memory overhead to handle real-world volumes of asynchronous events and log messages.

Specifically for trap processing, with just 8GB of memory for the Java Heap, OpenNMS Core can scale to handle over 2000 traps per second with no loss. That adds up too! A single 8GB (with a paltry 4 CPU cores) instance of OpenNMS could handle up to 172,800,000 traps per day, and that’s without saturating dependencies like PostgreSQL, Kafka, or our remote collectors (Minions).

One of the biggest advantages of OpenNMS’s architecture is that you can begin with a modest and lightweight deployment (just a single Core and a PostgreSQL database are all you need), then scale up or out as your monitoring needs grow and change.

Scaling OpenNMS isn’t just dependent on the resources for the OpenNMS core, though; we are reliant on external services like our core PostgreSQL database, message brokers (Kafka), storage, or indexing back-ends. This means that rather than loading everything into a single, memory-heavy box, you can break out the services you need and scale them each as you need to expand your monitoring needs.

In the context of a memory-scarce market, where DRAM prices have surged, and memory supply is expected to remain tight through 2027, this flexibility matters more than ever. You’re not forced to commit to a high-resource server up front. Instead, you can deploy with minimal resources and grow capacity only when and where it's needed.

Ready to see what OpenNMS can do for you? Contact us to set up a demo and learn how OpenNMS’s lean network monitoring system can work for your organization.