{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your inference service slows down when several customers send requests together.",
    "change": "Replay a recorded request trace with only the proposed serving policy changed.",
    "baseline": "Your current scheduler on the same model and hardware.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Requests meeting your latency target, throughput, failures, and cost per successful request.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2609.20957",
  "title": "An Approximate Queueing Model of LLM Inference Serving for SLO-Driven Autoscaling",
  "summary": "It replaces reactive or throughput-only autoscalers with an approximate three-parameter queueing model that predicts TTFT and ITL to set replica counts against explicit latency SLOs. The cost is per-model-accelerator parameter calibration from latency measurements, a state-dependent Markov chain solver in the control loop, and the multi-replica GPU cluster the controller exists to manage. It can fail when load exceeds the validated light-to-moderate regime, when Markovian assumptions break under bursty or non-stationary traffic, or when parameters drift across model, hardware, or serving-stack changes.",
  "rationale": "The technique targets multi-replica cluster autoscaling on H100 infrastructure, which the reader does not have; it is worth tracking as a capacity-planning method but cannot be adopted on a single 24 GB GPU or API-based setup.",
  "technique": "Queueing-model SLO-driven autoscaling for LLM serving",
  "family": "serving_e_batching",
  "recommendation": "observar",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.20957",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
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      "statement": "The model-based controller missed its latency target in 7 of 127 control cycles under a fourfold load ramp, versus 27 of 128 misses for a decode-throughput analyzer, with in-loop median prediction errors of at most 5% (TTFT) and 9% (ITL); note the baseline also provisioned 4% and 28% fewer replicas, so the comparison is not iso-cost.",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "dimension": "security",
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}