P3.1: GPU/Queue-aware routing — NCS metrics + scoring-based model selection

NCS (services/node-capabilities/metrics.py):
- NodeLoad: inflight_jobs, queue_depth, concurrency_limit, estimated_wait_ms,
  cpu_load_1m, mem_pressure (macOS + Linux), rtt_ms_to_hub
- RuntimeLoad: per-runtime healthy, p50_ms, p95_ms from rolling 50-sample window
- POST /capabilities/report_latency for node-worker → NCS reporting
- NCS fetches worker metrics via NODE_WORKER_URL

Node Worker:
- GET /metrics endpoint (inflight, concurrency, latency buffers)
- Latency tracking per job type (llm/vision) with rolling buffer
- Fire-and-forget latency reporting to NCS after each successful job

Router (model_select v3):
- score_candidate(): wait + model_latency + cross_node_penalty + prefer_bonus
- LOCAL_THRESHOLD_MS=250: prefer local if within threshold of remote
- ModelSelection.score field for observability
- Structured [score] logs with chosen node, model, and score breakdown

Tests: 19 new (12 scoring + 7 NCS metrics), 36 total pass
Docs: ops/runbook_p3_1.md, ops/CHANGELOG_FABRIC.md

No breaking changes to JobRequest/JobResponse or capabilities schema.

Made-with: Cursor

services/node-worker/main.py

@@ -26,6 +26,11 @@ async def healthz():
     }
 
 
+@app.get("/metrics")
+async def metrics():
+    return worker.get_metrics()
+
+
 @app.on_event("startup")
 async def startup():
     global _nats_client

services/node-worker/worker.py

@@ -2,6 +2,7 @@
 import asyncio
 import json
 import logging
+import os
 import time
 from typing import Any, Dict
 
@@ -15,6 +16,10 @@ logger = logging.getLogger("node-worker")
 _idem = IdempotencyStore()
 _semaphore: asyncio.Semaphore = asyncio.Semaphore(config.MAX_CONCURRENCY)
 _nats_client = None
+_inflight_count: int = 0
+_latencies_llm: list = []
+_latencies_vision: list = []
+_LATENCY_BUFFER = 50
 
 
 async def start(nats_client):
@@ -88,12 +93,25 @@ async def _handle_request(msg):
             await _reply(msg, resp)
             return
 
-        async with _semaphore:
-            resp = await _execute(job, remaining)
+        global _inflight_count
+        _inflight_count += 1
+        try:
+            async with _semaphore:
+                resp = await _execute(job, remaining)
+        finally:
+            _inflight_count -= 1
 
         _idem.put(idem_key, resp)
         _idem.complete_inflight(idem_key, resp)
         resp.latency_ms = int((time.time() - t0) * 1000)
+
+        if resp.status == "ok" and resp.latency_ms > 0:
+            buf = _latencies_llm if job.required_type in ("llm", "code") else _latencies_vision
+            buf.append(resp.latency_ms)
+            if len(buf) > _LATENCY_BUFFER:
+                del buf[:len(buf) - _LATENCY_BUFFER]
+            _report_latency_async(job.required_type, resp.provider or "ollama", resp.latency_ms)
+
         await _reply(msg, resp)
 
     except Exception as e:
@@ -179,6 +197,37 @@ async def _execute(job: JobRequest, remaining_ms: int) -> JobResponse:
         )
 
 
+def get_metrics() -> Dict[str, Any]:
+    return {
+        "inflight_jobs": _inflight_count,
+        "concurrency_limit": config.MAX_CONCURRENCY,
+        "queue_depth": 0,
+        "last_latencies_llm": list(_latencies_llm[-_LATENCY_BUFFER:]),
+        "last_latencies_vision": list(_latencies_vision[-_LATENCY_BUFFER:]),
+    }
+
+
+def _report_latency_async(req_type: str, runtime: str, latency_ms: int):
+    """Fire-and-forget latency report to local NCS."""
+    import httpx as _httpx
+
+    ncs_url = os.getenv("NCS_REPORT_URL", "http://node-capabilities:8099")
+
+    async def _do():
+        try:
+            async with _httpx.AsyncClient(timeout=1) as c:
+                await c.post(f"{ncs_url}/capabilities/report_latency", json={
+                    "runtime": runtime, "type": req_type, "latency_ms": latency_ms,
+                })
+        except Exception:
+            pass
+
+    try:
+        asyncio.get_event_loop().create_task(_do())
+    except RuntimeError:
+        pass
+
+
 async def _reply(msg, resp: JobResponse):
     if msg.reply:
         await _nats_client.publish(msg.reply, resp.model_dump_json().encode())