{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "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": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "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": "2610.26160",
  "title": "Holistic Buoyancy-Driven Cloud Native Resource Management",
  "summary": "The method replaces static resource requests and prior workload profiling in Kubernetes scheduling with runtime 'buoyancy' signals that measure each workload's sensitivity to shared resources such as last-level cache and memory bandwidth, driving adaptive scheduling, node-local allocation, and limited rescheduling. It costs a Kubernetes-native control plane extension, continuous runtime instrumentation of workloads, and a multi-node cluster to provide rescheduling headroom, plus ongoing maintenance of the sensitivity-measurement framework. It can fail when buoyancy estimates are inaccurate or unstable under rapidly shifting workload mixes, when rescheduling triggers migration overhead or disruption, and its benefits are unproven outside the evaluated congestion regimes.",
  "rationale": "This targets multi-tenant cluster operators rather than single-GPU or API-based LLM deployment, so the reader lacks the infrastructure and problem setting to adopt it, though the runtime-contention-sensitivity idea is worth monitoring.",
  "technique": "Buoyancy-driven Kubernetes resource management",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-10-19",
  "as_of": "2026-10-20",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.26160",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces static resource requests and prior workload profiling in Kubernetes scheduling with runtime 'buoyancy' signals that measure each workload's sensitivity to shared resources such as last-level cache and memory bandwidth, driving adaptive scheduling, node-local allocation, and limited rescheduling. It costs a Kubernetes-native control plane extension, continuous runtime instrumentation of workloads, and a multi-node cluster to provide rescheduling headroom, plus ongoing maintenance of the sensitivity-measurement framework. It can fail when buoyancy estimates are inaccurate or unstable under rapidly shifting workload mixes, when rescheduling triggers migration overhead or disruption, and its benefits are unproven outside the evaluated congestion regimes.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.26160",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "In a high-congestion scenario, the share of workloads meeting their SLOs rises from 7.74% to 87.8% on average versus the Kubernetes baseline, with reduced congestion, SLO violation severity, and cluster fragmentation.",
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.26160",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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    },
    {
      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}