{
  "status": "research",
  "advantages": [],
  "limitations": [],
  "supersedes": [],
  "supersededBy": [],
  "firstSeen": null,
  "lastSeen": null,
  "updated": "2026-10-15",
  "citations": [
    {
      "id": "mahdisoltani2017",
      "title": "Mahdisoltani, Stefanovici and Schroeder. Improving Storage System Reliability with Proactive Error Prediction. USENIX ATC 2017, sections 4.1-4.3.",
      "url": "https://www.usenix.org/system/files/conference/atc17/atc17-mahdisoltani.pdf",
      "accessed": "2026-10-15"
    },
    {
      "id": "hesela-budget",
      "title": "Hesela: sector-error prediction and adaptive scrub-budget arithmetic.",
      "url": "https://hesela.com/analyses/sector-error-prediction-scrubbing/",
      "accessed": "2026-10-15"
    }
  ],
  "slug": "adaptive-scrubbing",
  "term": "Adaptive scrubbing",
  "aliases": [
    "Prediction-guided scrubbing"
  ],
  "definition": "Adaptive scrubbing adjusts the rate or schedule of proactive storage verification in response to observed conditions or estimated error risk.",
  "note": "Distinguish classifier false-positive rate from the fraction of time spent scanning faster. An alert budget must include true alerts and alert duration, not just false alarms. The ATC 2017 evaluation was a simulation, not a measurement of reduced production data loss. [mahdisoltani2017] Equal-duration examples and their assumptions are available in the accompanying arithmetic note. [hesela-budget]",
  "related": [
    "data-scrubbing",
    "mean-time-to-detection",
    "false-positive-rate",
    "class-prevalence",
    "recall"
  ]
}