Hi everyone! I'm still pretty new to the whole DevOps cost optimization side of things, and I could use some guidance.
Our team's main Jenkins controller is running on an AWS `m5.4xlarge` instance (16 vCPUs, 64 GiB RAM). It feels massively over-provisioned most of the time, but during big pipeline runs, it does spike. We're trying to control cloud costs, and this seems like a good place to start. How do you figure out the right size?
What metrics should I be looking at to make a data-driven decision? I've started collecting some basic stats from the instance itself, but I'm not sure what's most important for Jenkins specifically.
Here's a snippet of the simple monitoring I set up to track CPU/Memory over the last week:
```bash
# This runs via a cron job
timestamp=$(date '+%Y-%m-%d %H:%M:%S')
cpu=$(top -bn1 | grep "Cpu(s)" | awk '{print $2}' | cut -d'%' -f1)
mem_used=$(free -m | awk 'NR==2{printf "%.2f", $3*100/$2 }')
echo "$timestamp, CPU: $cpu%, Mem: $mem_used%" >> /var/log/instance_metrics.log
```
Is this the right approach? Should I be looking at Jenkins-specific metrics like executor usage or queue length instead? Any beginner-friendly tips on how to analyze this and downsize safely would be so appreciated! 🙏
That's a sensible start for system-level metrics, but you're right to suspect Jenkins itself provides more actionable signals. CPU and memory spikes are often symptoms, not causes. You need to correlate them with Jenkins' internal load.
Focus on these Jenkins metrics, which you can get via the `/metrics` endpoint or the Monitoring plugin:
- `jenkins.executor.count.value` and `jenkins.executor.in.use.value`: This shows your active concurrency. If `in.use` is consistently far below `count`, you have over-provisioned executors on the controller.
- `jenkins.queue.length.value`: A consistently high queue length indicates the controller is a bottleneck, but if it's usually zero except during those big pipeline runs, your issue is burst capacity.
- `jenkins.node.online.total` and `jenkins.node.offline.total`: Controller stress is often relieved by offloading work to agent nodes. Ensure your agents are healthy and sufficient.
Your current script samples at a single point in time. You'll miss short, sharp spikes. Use CloudWatch (or Prometheus/Grafana if you have it) to capture metrics at a one-minute granularity over at least two weeks to catch your full pipeline cycle.
A methodical approach is to create a simple dashboard plotting system CPU/memory alongside those Jenkins metrics. You'll likely see that the big spikes correspond directly to high executor usage and queue length. That correlation tells you if you need a bigger instance, or just need to tune Jenkins' own resource consumption (e.g., limit executor count on the master, force more work to agents) and maybe move to a smaller instance type. The `m5.4xlarge` is quite large for a controller; often the controller's job is orchestration, not execution.