Kjell Tore Guttormsen
dd1036ab8a
Task #5 del 1/3 (URL-migrering). Verifiseringen motbeviste STATE.md-premisset om ren prefix-swap: rebrand er per-URL, ikke mekanisk. En blind sed ai-foundry→foundry ville lagd 56 nye 404-er (classic-stiene finnes ikke under nytt foundry/-prefiks — bekreftet empirisk). Metode: resolverte alle 237 unike KB-URLer mot live redirects (curl -L), bygde full-URL→full-URL-mapping fra faktisk url_effective. Bevarer locale-form, query (?view=) og #fragment per lenke. - 231 navnerom-erstatninger over 141 filer (408 forekomster): - 161 → azure/foundry/ (98 ren prefix-swap + 10 sti-reorg + reorg-tilfeller) - 69 → azure/foundry-classic/ (eldre hub-spor: assistants, hub-DR, on-your-data; faktisk redirect-mål per operatorvalg) - 1 → azure/foundry-local/ - 2 døde lenker (404) fikset til verifiserte mål: - agent-service → azure/foundry/agents/overview - concepts/evaluation-evaluators/ → azure/foundry/how-to/evaluate-generative-ai-app - 5 path-/display-referanser (uten https://, i backticks/lenketekst) rettet manuelt. - 6 slug-baserte ai-foundry-treff urørt (scope-grense): managed-grafana-dashboard, security-baseline, power-platform prompt-builder, architecture baseline-chat (sistnevnte slug-rebrand i annet navnerom — mulig fremtidig funn). - Parkert til task #5 del 2/3: Norway East GPT-5-datasuverenitet-fiks + modellkatalog-utvidelse (5.3/5.4/5.5, gpt-oss, sora-2). Verifisert: 0 gjenværende azure/ai-foundry/-navnerom i skills/. validate-plugin.sh 219 PASS. test-kb-integrity.sh 117/117 passed. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01REiKFhP4w6xGXXqWKpPCJJ
431 lines
15 KiB
Markdown
431 lines
15 KiB
Markdown
# Concurrent Request Optimization
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**Last updated:** 2026-02
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**Status:** GA
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**Category:** Performance & Scalability
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---
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## Introduksjon
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Concurrent request optimization handler om å maksimere antall samtidige forespørsler mot Azure OpenAI uten å overbelaste tjenesten eller miste forespørsler. Den optimale graden av samtidighet avhenger av deployment-type (Standard vs. PTU), tildelt kvote (TPM/RPM), modellens responstid og klientens evne til å håndtere parallelle forbindelser.
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For Standard deployments bestemmer RPM-kvoten den harde grensen for samtidige forespørsler per minutt, men den faktiske grensen er ofte lavere fordi lange forespørsler blokkerer kapasitet. For PTU deployments er grensen definert av utilization — når prosessert kapasitet nærmer seg 100% av tildelte PTUs, begynner 429-feil. I begge tilfeller er nøkkelen å finne sweet spot der throughput er maksimert uten overdreven throttling.
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For norsk offentlig sektor, der AI-applikasjoner kan betjene hundrevis av samtidige saksbehandlere, er concurrent request optimization avgjørende for å sikre jevn brukeropplevelse uten at noen opplever timeout eller feil.
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## Kjernekomponenter
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| Komponent | Formål | Teknologi |
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|-----------|--------|-----------|
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| Semaphore | Begrens concurrent requests klient-side | asyncio / SemaphoreSlim |
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| Token Bucket | Rate limiting med burst-støtte | Custom / APIM |
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| Connection Pool | Gjenbruk HTTP-forbindelser | HttpClient / aiohttp |
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| Circuit Breaker | Forhindre kaskade ved overbelastning | Polly / custom |
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| Queue | Buffer forespørsler under peak | Service Bus / in-memory |
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## Concurrency Level Tuning
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### Finn optimal concurrency
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```python
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import asyncio
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import time
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from openai import AsyncAzureOpenAI, RateLimitError
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async def find_optimal_concurrency(
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client: AsyncAzureOpenAI,
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model: str = "gpt-4o",
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test_prompt: str = "Oppsummer dette kort.",
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max_tokens: int = 200,
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test_levels: list[int] = None,
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requests_per_level: int = 50
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) -> dict:
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"""Find optimal concurrency level through progressive testing."""
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if test_levels is None:
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test_levels = [1, 5, 10, 20, 30, 50, 75, 100]
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results = []
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for concurrency in test_levels:
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semaphore = asyncio.Semaphore(concurrency)
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stats = {"success": 0, "throttled": 0, "errors": 0, "latencies": []}
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async def send_one():
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async with semaphore:
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start = time.time()
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try:
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await client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": test_prompt}],
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max_tokens=max_tokens
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)
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stats["latencies"].append(
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(time.time() - start) * 1000)
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stats["success"] += 1
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except RateLimitError:
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stats["throttled"] += 1
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except Exception:
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stats["errors"] += 1
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start = time.time()
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await asyncio.gather(
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*[send_one() for _ in range(requests_per_level)])
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duration = time.time() - start
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total = stats["success"] + stats["throttled"] + stats["errors"]
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throttle_rate = stats["throttled"] / max(total, 1) * 100
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result = {
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"concurrency": concurrency,
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"throughput_rps": round(stats["success"] / duration, 2),
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"throttle_rate_pct": round(throttle_rate, 1),
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"p50_ms": round(sorted(stats["latencies"])[
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len(stats["latencies"]) // 2], 0)
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if stats["latencies"] else 0,
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"p95_ms": round(sorted(stats["latencies"])[
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int(len(stats["latencies"]) * 0.95)], 0)
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if stats["latencies"] else 0,
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"success": stats["success"],
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"throttled": stats["throttled"]
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}
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results.append(result)
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print(f"Concurrency {concurrency}: "
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f"{result['throughput_rps']} RPS, "
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f"{throttle_rate:.1f}% throttled, "
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f"P50={result['p50_ms']}ms")
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# Stopp hvis throttle rate er for høy
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if throttle_rate > 30:
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print(f"Stopping: throttle rate too high at {concurrency}")
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break
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# Finn optimal: best throughput med <5% throttling
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acceptable = [r for r in results if r["throttle_rate_pct"] < 5]
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if acceptable:
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optimal = max(acceptable, key=lambda r: r["throughput_rps"])
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else:
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optimal = results[0]
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return {
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"optimal_concurrency": optimal["concurrency"],
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"optimal_throughput_rps": optimal["throughput_rps"],
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"all_results": results
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}
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```
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### Adaptive concurrency control
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```python
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class AdaptiveConcurrencyController:
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"""Dynamically adjust concurrency based on response signals."""
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def __init__(
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self,
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initial_concurrency: int = 10,
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min_concurrency: int = 1,
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max_concurrency: int = 100,
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increase_threshold: float = 0.02, # Øk hvis <2% throttled
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decrease_threshold: float = 0.10, # Reduser hvis >10% throttled
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adjustment_interval: float = 10.0 # Juster hvert 10. sekund
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):
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self.current = initial_concurrency
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self.min_concurrency = min_concurrency
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self.max_concurrency = max_concurrency
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self.increase_threshold = increase_threshold
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self.decrease_threshold = decrease_threshold
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self.adjustment_interval = adjustment_interval
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self._semaphore = asyncio.Semaphore(initial_concurrency)
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self._window_success = 0
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self._window_throttled = 0
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self._last_adjustment = time.time()
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async def acquire(self):
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"""Acquire a concurrency slot."""
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await self._semaphore.acquire()
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def release(self, was_throttled: bool = False):
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"""Release slot and record outcome."""
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self._semaphore.release()
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if was_throttled:
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self._window_throttled += 1
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else:
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self._window_success += 1
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self._maybe_adjust()
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def _maybe_adjust(self):
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"""Periodically adjust concurrency."""
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now = time.time()
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if now - self._last_adjustment < self.adjustment_interval:
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return
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total = self._window_success + self._window_throttled
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if total < 10: # Ikke nok data
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return
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throttle_rate = self._window_throttled / total
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old = self.current
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if throttle_rate < self.increase_threshold:
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# Trygt å øke
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self.current = min(
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self.current + max(1, self.current // 10),
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self.max_concurrency)
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elif throttle_rate > self.decrease_threshold:
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# Må redusere
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self.current = max(
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self.current - max(1, self.current // 5),
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self.min_concurrency)
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if self.current != old:
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# Opprett ny semaphore med justert limit
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self._semaphore = asyncio.Semaphore(self.current)
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print(f"Concurrency adjusted: {old} → {self.current} "
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f"(throttle rate: {throttle_rate:.1%})")
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self._window_success = 0
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self._window_throttled = 0
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self._last_adjustment = now
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```
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## Request Queueing Strategies
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### Priority queue for AI-forespørsler
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```python
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import asyncio
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import heapq
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from enum import IntEnum
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from dataclasses import dataclass, field
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from typing import Any
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class Priority(IntEnum):
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URGENT = 1
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HIGH = 2
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NORMAL = 3
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LOW = 4
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BACKGROUND = 5
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@dataclass(order=True)
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class PrioritizedRequest:
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priority: int
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timestamp: float = field(compare=True)
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request: Any = field(compare=False)
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future: asyncio.Future = field(compare=False, repr=False)
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class PriorityRequestQueue:
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"""Priority queue for AI requests with concurrency control."""
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def __init__(self, max_concurrent: int = 20):
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self._queue: list[PrioritizedRequest] = []
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self._semaphore = asyncio.Semaphore(max_concurrent)
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self._processing = True
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async def submit(
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self,
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request: dict,
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priority: Priority = Priority.NORMAL
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) -> asyncio.Future:
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"""Submit request with priority. Returns future."""
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future = asyncio.get_event_loop().create_future()
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item = PrioritizedRequest(
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priority=priority.value,
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timestamp=time.time(),
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request=request,
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future=future
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)
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heapq.heappush(self._queue, item)
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return future
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async def process_loop(self, process_fn):
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"""Continuously process queued requests."""
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while self._processing:
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if not self._queue:
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await asyncio.sleep(0.01)
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continue
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await self._semaphore.acquire()
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item = heapq.heappop(self._queue)
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asyncio.create_task(
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self._process_item(item, process_fn))
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async def _process_item(self, item, process_fn):
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try:
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result = await process_fn(item.request)
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if not item.future.done():
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item.future.set_result(result)
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except Exception as e:
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if not item.future.done():
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item.future.set_exception(e)
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finally:
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self._semaphore.release()
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```
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## Deadlock Prevention
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### Unngå resource starvation
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```python
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class DeadlockPreventionWrapper:
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"""Prevent deadlocks in concurrent AI request processing."""
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def __init__(
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self,
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client: AsyncAzureOpenAI,
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max_concurrent: int = 20,
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request_timeout: float = 120.0,
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starvation_timeout: float = 300.0
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):
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self.client = client
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self.semaphore = asyncio.Semaphore(max_concurrent)
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self.request_timeout = request_timeout
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self.starvation_timeout = starvation_timeout
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self._active_requests: dict[str, float] = {}
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async def execute(self, request_id: str, **kwargs):
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"""Execute with timeout and starvation protection."""
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# Timeout på semaphore acquire — forhindrer deadlock
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try:
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await asyncio.wait_for(
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self.semaphore.acquire(),
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timeout=self.starvation_timeout
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)
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except asyncio.TimeoutError:
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raise TimeoutError(
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f"Request {request_id} starved waiting for "
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f"concurrency slot for {self.starvation_timeout}s. "
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f"Consider increasing max_concurrent or reducing "
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f"request volume.")
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self._active_requests[request_id] = time.time()
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try:
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# Timeout på selve forespørselen
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result = await asyncio.wait_for(
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self.client.chat.completions.create(**kwargs),
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timeout=self.request_timeout
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)
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return result
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except asyncio.TimeoutError:
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raise TimeoutError(
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f"Request {request_id} timed out after "
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f"{self.request_timeout}s")
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finally:
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self._active_requests.pop(request_id, None)
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self.semaphore.release()
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@property
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def active_count(self) -> int:
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return len(self._active_requests)
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def get_stuck_requests(self, threshold_seconds: float = 60) -> list:
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"""Identify requests that may be stuck."""
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now = time.time()
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return [
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{"id": rid, "age_seconds": round(now - start, 1)}
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for rid, start in self._active_requests.items()
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if now - start > threshold_seconds
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]
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```
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## Resource Contention Resolution
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### Token bucket for fair scheduling
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```python
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import time
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class TokenBucket:
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"""Token bucket rate limiter for fair resource sharing."""
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def __init__(
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self,
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tokens_per_second: float,
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max_burst: int = 10
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):
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self.rate = tokens_per_second
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self.max_burst = max_burst
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self._tokens = max_burst
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self._last_refill = time.time()
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self._lock = asyncio.Lock()
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async def acquire(self, tokens: int = 1) -> float:
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"""Acquire tokens, waiting if necessary. Returns wait time."""
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async with self._lock:
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self._refill()
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if self._tokens >= tokens:
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self._tokens -= tokens
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return 0
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# Beregn ventetid
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deficit = tokens - self._tokens
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wait_time = deficit / self.rate
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await asyncio.sleep(wait_time)
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self._refill()
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self._tokens -= tokens
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return wait_time
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def _refill(self):
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now = time.time()
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elapsed = now - self._last_refill
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self._tokens = min(
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self.max_burst,
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self._tokens + elapsed * self.rate
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)
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self._last_refill = now
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class FairScheduler:
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"""Fair scheduling across multiple tenants/users."""
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def __init__(self, total_rps: float, num_tenants: int):
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self.total_rps = total_rps
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per_tenant_rps = total_rps / num_tenants
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self.buckets: dict[str, TokenBucket] = {}
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self._default_rps = per_tenant_rps
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def get_bucket(self, tenant_id: str) -> TokenBucket:
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if tenant_id not in self.buckets:
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self.buckets[tenant_id] = TokenBucket(
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tokens_per_second=self._default_rps,
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max_burst=int(self._default_rps * 2)
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)
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return self.buckets[tenant_id]
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```
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## Norsk offentlig sektor
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- **Fair use**: I multi-tenant løsninger der flere enheter deler samme Azure OpenAI-deployment, bruk per-tenant rate limiting for å sikre rettferdig fordeling.
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- **Brukeropplevelse**: Sett starvation timeout til maks ventetid brukere aksepterer (typisk 30-60 sekunder). Returner informativ feilmelding ved timeout.
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- **Overvåking**: Logg concurrent request-nivå, kø-dybde og starvation-hendelser i Application Insights for kapasitetsplanlegging.
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- **Skalering**: Planlegg for peak-perioder (morgen 08-10, etter lunsj 12-13) med høyere concurrent limits eller ekstra kvote.
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## Beslutningsrammeverk
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| Scenario | Anbefaling | Begrunnelse |
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|----------|------------|-------------|
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| Ukjent workload | Start med 10 concurrent, juster | Unngå initial throttling |
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| Forutsigbar, jevn trafikk | Statisk semaphore på optimal nivå | Enklest å implementere |
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| Variable peaks | Adaptive concurrency controller | Automatisk tilpasning |
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| Multi-tenant | Priority queue + per-tenant bucket | Fair resource sharing |
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| Kritisk latens | Lav concurrency + PTU | Forutsigbar responstid |
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## Referanser
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- [Manage Azure OpenAI quota](https://learn.microsoft.com/azure/foundry-classic/openai/how-to/quota) — RPM/TPM grenser
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- [Performance and latency](https://learn.microsoft.com/azure/foundry/openai/how-to/latency) — Concurrent requests og throughput
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- [Provisioned throughput](https://learn.microsoft.com/azure/foundry/openai/how-to/provisioned-get-started) — PTU utilization
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## For Cosmo
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- **Bruk denne referansen** når kunden opplever timeout, starvation eller ujevn ytelse i AI-applikasjoner med mange samtidige brukere.
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- Start konservativt (10-20 concurrent) og øk gradvis mens du monitorerer throttle rate — aldri gå rett til 100 concurrent.
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- Adaptive concurrency control er anbefalt for produksjon — statiske verdier fungerer dårlig når trafikkmønstre endres.
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- Prioritetskøer er viktige for multi-tenant: sørg for at kritiske oppgaver (saksbehandler-beslutninger) ikke blokkeres av bakgrunnsjobber.
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- Deadlock prevention med timeouts er obligatorisk — uten det kan en hengende forespørsel blokkere alle slots permanent.
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