# Predictive Resource Allocation

Forecasts near-term fleet demand from historical telemetry so the operator can
pre-provision resources before a surge hits.

## How It Works

The predictor reads two data sources:

1. **Metric logs** (`metrics/local_*.jsonl`) — request cadence, token volume,
   caller mix, success/failure rates
2. **Heartbeat logs** (`heartbeat/ticks_*.jsonl`) — Gitea availability,
   local inference health

It compares a **recent window** (last N hours of activity) against the **previous active window**
(previous N hours ending at the most recent event before the current window) so sparse telemetry still yields a meaningful baseline.

## Output Contract

```json
{
  "resource_mode": "steady|surge",
  "dispatch_posture": "normal|degraded",
  "horizon_hours": 6,
  "recent_request_rate": 12.5,
  "baseline_request_rate": 8.0,
  "predicted_request_rate": 15.0,
  "surge_factor": 1.56,
  "demand_level": "elevated|normal|low|critical",
  "gitea_outages": 0,
  "inference_failures": 2,
  "top_callers": [...],
  "recommended_actions": ["..."]
}
```

### Demand Levels

| Surge Factor | Level | Meaning |
|-------------|-------|---------|
| > 3.0 | critical | Extreme surge, immediate action needed |
| > 1.5 | elevated | Notable increase, pre-warm recommended |
| > 1.0 | normal | Slight increase, monitor |
| <= 1.0 | low | Flat or declining |

### Posture Signals

| Signal | Effect |
|--------|--------|
| Surge factor > 1.5 | `resource_mode: surge` + pre-warm recommendation |
| Gitea outages >= 1 | `dispatch_posture: degraded` + cache recommendation |
| Inference failures >= 2 | `resource_mode: surge` + reliability investigation |
| Heavy batch callers | Throttle recommendation |
| High caller failure rates | Investigation recommendation |

## Usage

```bash
# Markdown report
python3 scripts/predictive_resource_allocator.py

# JSON output
python3 scripts/predictive_resource_allocator.py --json

# Custom paths and horizon
python3 scripts/predictive_resource_allocator.py \
    --metrics metrics/local_20260329.jsonl \
    --heartbeat heartbeat/ticks_20260329.jsonl \
    --horizon 12
```

## Tests

```bash
python3 -m pytest tests/test_predictive_resource_allocator.py -v
```

## Recommended Actions

The predictor generates contextual recommendations:

- **Pre-warm local inference** — surge detected, warm up before next window
- **Throttle background jobs** — heavy batch work consuming capacity
- **Investigate failure rates** — specific callers failing at high rates
- **Investigate model reliability** — inference health degraded
- **Cache forge state** — Gitea availability issues
- **Maintain current allocation** — no issues detected