Add pi50 resource optimization plan, mark monitoring design complete

- New plan: Improve pi50 control plane resource usage
- Completed: Workstation monitoring design status file

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
OpenCode Test
2026-01-05 13:00:57 -08:00
parent 5b9a85cd37
commit f9e9be62bc
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{
"plan": "2026-01-05-workstation-monitoring-design.md",
"status": "COMPLETE",
"completed_at": "2026-01-05T14:09:00Z",
"implementation": {
"node_exporter": "installed and running (v1.10.2-1)",
"scrape_config": "deployed (workstation-scrape)",
"prometheus_rule": "deployed (workstation-alerts, 12 rules)",
"prometheus_target": "UP and scraping",
"git_commit": "9d17ac8",
"network_solution": "Tailscale (100.90.159.78:9100)"
},
"verification": {
"all_success_criteria_met": true,
"verified_at": "2026-01-05T14:09:19Z"
}
}

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# Plan: Improve pi50 (Control Plane) Resource Usage
## Problem Summary
pi50 (control plane) is running at **73% CPU / 81% memory** while worker nodes have significant headroom:
- pi3: 7% CPU / 65% memory (but only 800MB RAM - memory constrained)
- pi51: 18% CPU / 64% memory (8GB RAM - plenty of capacity)
**Root cause**: pi50 has **NO control-plane taint**, so the scheduler treats it as a general worker node. It currently runs ~85 pods vs 38 on pi51.
## Current State
| Node | Role | CPUs | Memory | CPU Used | Mem Used | Pods |
|------|------|------|--------|----------|----------|------|
| pi50 | control-plane | 4 | 8GB | 73% | 81% | ~85 |
| pi3 | worker | 4 | 800MB | 7% | 65% | 13 |
| pi51 | worker | 4 | 8GB | 18% | 64% | 38 |
## Recommended Approach
### Option A: Add PreferNoSchedule Taint (Recommended)
Add a soft taint to pi50 that tells the scheduler to prefer other nodes for new workloads, while allowing existing pods to remain.
```bash
kubectl taint nodes pi50 node-role.kubernetes.io/control-plane=:PreferNoSchedule
```
**Pros:**
- Non-disruptive - existing pods continue running
- New pods will prefer pi51/pi3
- Gradual rebalancing as pods are recreated
- Easy to remove if needed
**Cons:**
- Won't immediately reduce load
- Existing pods stay where they are
### Option B: Move Heavy Workloads Immediately
Identify and relocate the heaviest workloads from pi50 to pi51:
**Top CPU consumers on pi50:**
1. ArgoCD application-controller (157m CPU, 364Mi) - should stay (manages cluster)
2. Longhorn instance-manager (139m CPU, 707Mi) - must stay (storage)
3. ai-stack workloads (ollama, litellm, open-webui, etc.)
**Candidates to move to pi51:**
- `ai-stack/ollama` - can run on any node with storage
- `ai-stack/litellm` - stateless, can move
- `ai-stack/open-webui` - can move
- `ai-stack/claude-code`, `codex`, `gemini-cli`, `opencode` - can move
- `minio` - can move (uses PVC)
- `pihole2` - can move
**Method**: Add `nodeSelector` or `nodeAffinity` to deployments:
```yaml
spec:
template:
spec:
nodeSelector:
kubernetes.io/hostname: pi51
```
Or use anti-affinity to avoid pi50:
```yaml
spec:
template:
spec:
affinity:
nodeAffinity:
preferredDuringSchedulingIgnoredDuringExecution:
- weight: 100
preference:
matchExpressions:
- key: node-role.kubernetes.io/control-plane
operator: DoesNotExist
```
### Option C: Combined Approach (Best)
1. Add `PreferNoSchedule` taint to pi50 (prevents future imbalance)
2. Immediately move 2-3 heaviest moveable workloads to pi51
3. Let remaining workloads naturally migrate over time
## Execution Steps
### Step 1: Add taint to pi50
```bash
kubectl taint nodes pi50 node-role.kubernetes.io/control-plane=:PreferNoSchedule
```
### Step 2: Verify existing workloads still running
```bash
kubectl get pods -A -o wide --field-selector spec.nodeName=pi50 | grep -v Running
```
### Step 3: Move heavy ai-stack workloads (optional, for immediate relief)
For each deployment to move, patch with node anti-affinity or selector:
```bash
kubectl patch deployment -n ai-stack ollama --type=merge -p '{"spec":{"template":{"spec":{"nodeSelector":{"kubernetes.io/hostname":"pi51"}}}}}'
```
Or delete pods to trigger rescheduling (if PreferNoSchedule taint is set):
```bash
kubectl delete pod -n ai-stack <pod-name>
```
### Step 4: Monitor
```bash
kubectl top nodes
```
## Workloads That MUST Stay on pi50
- `kube-system/*` - Core cluster components
- `longhorn-system/csi-*` - Storage controllers
- `longhorn-system/longhorn-driver-deployer` - Storage management
- `local-path-storage/*` - Local storage provisioner
## Expected Outcome
After changes:
- pi50: ~50-60% CPU, ~65-70% memory (control plane + essential services)
- pi51: ~40-50% CPU, ~70-75% memory (absorbs application workloads)
- New pods prefer pi51 automatically
## Risks
- **Low**: PreferNoSchedule is a soft taint - pods with tolerations can still schedule on pi50
- **Low**: Moving workloads may cause brief service interruption during pod recreation
- **Note**: pi3 cannot absorb much due to 800MB RAM limit
## Selected Approach: A + B (Combined)
User selected combined approach:
1. Add `PreferNoSchedule` taint to pi50
2. Move heavy ai-stack workloads to pi51 immediately
## Execution Plan
### Phase 1: Add Taint
```bash
kubectl taint nodes pi50 node-role.kubernetes.io/control-plane=:PreferNoSchedule
```
### Phase 2: Move Heavy Workloads to pi51
Target workloads (heaviest on pi50):
- `ai-stack/ollama`
- `ai-stack/open-webui`
- `ai-stack/litellm`
- `ai-stack/claude-code`
- `ai-stack/codex`
- `ai-stack/gemini-cli`
- `ai-stack/opencode`
- `ai-stack/searxng`
- `minio/minio`
Method: Delete pods to trigger rescheduling (taint will push them to pi51):
```bash
kubectl delete pod -n ai-stack -l app.kubernetes.io/name=ollama
# etc for each workload
```
### Phase 3: Verify
```bash
kubectl top nodes
kubectl get pods -A -o wide | grep -E "ollama|open-webui|litellm"
```