## Context
Uptime Kuma TTFB was bimodal — fast ~150ms responses mixed with slow
~3s responses — median 1.7s, p95 3.2s across 20 samples. CPU request
was 50m (5% of one core) against a Node.js process that handles ~190
monitors plus SQLite DB maintenance. Memory request was 64Mi while
actual RSS sat around 221Mi, so the pod was also running above its
guaranteed memory floor and subject to eviction pressure when nodes got
tight.
CPU limits are intentionally absent cluster-wide (CFS throttling caused
more pain than it solved), so the only knob to give the scheduler a
higher floor is the request itself. Raising the request makes the node
reserve more CPU for the pod and lets the kernel's CFS weight it more
generously when the node is busy — should reduce the tail on the slow
path without introducing throttling.
## This change
- requests.cpu: 50m -> 100m
- requests.memory: 64Mi -> 128Mi
- limits.memory: unchanged at 512Mi
- limits.cpu: still unset (explicit — cluster-wide rule)
## What is NOT in this change
- No CPU limit added
- No readiness/liveness probe tuning
- No replica count change (still 1, Recreate strategy)
- No DB layer / SQLite tuning
## Measurements (20 curl samples of https://uptime.viktorbarzin.me/)
Before:
min 0.143s
median 1.727s
p95 3.163s
max 3.204s
mean 1.768s
After:
min 0.149s
median 1.228s
p95 3.154s
max 3.283s
mean 1.590s
Median dropped ~29% (1.73s -> 1.23s). Tail (p95/max) essentially
unchanged — the slow bucket appears driven by something other than
CPU scheduling (likely socket.io / SSR render path inside the app,
or TLS/cf-tunnel handshake — worth a separate investigation).
Closes: code-79d
dbf7732a66