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Multi-view Feature-based SSD Failure Prediction: What, When, and Why

on YouTube 17m
Talkat FAST '23: 21st USENIX Conference on File and Storage Technologies · 2023-02-21..02-23· [machine-learning]

speakers

name:Yuqi Zhang
links:none on file
name:Wenwen Hao
links:none on file
name:Ben Niu
links:none on file
name:Kangkang Liu
links:none on file
name:Shuyang Wang
links:none on file
name:Na Liu
links:none on file
name:Xing He
links:none on file
name:Yongwong Gwon
links:none on file
name:Chankyu Koh
links:none on file

resources

video:https://youtu.be/Daa5E083Sps
slides:none on file

summary

Proposes MVTRF multi-view multi-task random forest that concatenates per-log attributes with histogram and sequence features extracted from long-term and short-term telemetry, classifies failures into eight types and predicts remaining lifespan as time segments. Trains offline and runs online; evaluates on ~70 million telemetry records from 300,000 SSDs (Tencent and public datasets) and reports average improvements of 46.1% precision / 57.4% recall over prior schemes. Uses similar decision extraction to surface key tree rules for root-cause clues and keeps the pipeline interpretable.

AI-written summary · about

chapters (5)

  1. 0:00Introduction
  2. 3:13Analysis and Design (3)
  3. 8:46Implementation
  4. 12:30Evaluation
  5. 16:13Conclusion

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