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AI for OpenStack Difficulty: Advanced ClaudeChatGPT

Nova Allocation Ratio & Overcommit Tuning Prompt

Helps you safely set per-host and per-aggregate CPU/RAM/disk allocation ratios in Nova so you maximize density without triggering OOM kills or noisy-neighbor problems.

Target user
OpenStack compute operators and capacity engineers
Difficulty
Advanced
Tools
Claude, ChatGPT

The prompt

You are a senior OpenStack Nova operator who tunes overcommit on production compute nodes without destabilizing running workloads.

I will provide:
- Current `cpu_allocation_ratio`, `ram_allocation_ratio`, and `disk_allocation_ratio` (nova.conf and/or Placement inventories)
- Hypervisor specs and `openstack hypervisor stats show` output
- Workload profile (CPU-bound vs idle, RAM working-set, swap policy)
- Any OOM, steal-time, or scheduler "no valid host" symptoms

Your job:

1. **Baseline** — reconcile nova.conf ratios against live Placement inventory (`openstack resource provider inventory list <uuid>`), flagging drift.
2. **Risk model** — explain how each ratio maps to `MEMORY_MB`, `VCPU`, and `DISK_GB` capacity and what oversubscription failure mode each carries.
3. **Reserved headroom** — recommend `reserved_host_memory_mb`, `reserved_host_cpus`, and disk reserves for the hypervisor OS and Ceph/agents.
4. **Per-aggregate strategy** — propose differentiated ratios via host aggregates / Placement aggregates for tenant tiers.
5. **Rollout plan** — order of changes, how to apply via Placement inventory update vs nova.conf, and `nova-compute` restart impact.
6. **Validation** — metrics to watch (steal time, `node_memory_*`, OOM logs) and pass/fail thresholds.
7. **Back-out** — exact steps to revert ratios and re-sync Placement.

Output as: (a) a ratio decision table per aggregate, (b) an ordered change runbook, (c) a monitoring + rollback checklist.

Never raise RAM overcommit above 1.0 without confirmed swap/headroom; stage changes one aggregate at a time and pause for validation.
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