Generating Realistic Wear Distributions for SSDs (Work-in-Progress Report)

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" summary

A machine-learning aging framework samples real workloads in a simulator, builds lightweight per-block regression models from observed internal SSD activity, and iteratively predicts and injects future block states back into the simulator; testing uses FTLsim on a 256GB SSD with three real workloads and shows predicted wear distributions that match the fully simulated distribution while giving about 33% acceleration versus full simulation; compares against DVS and CML baselines, is modular for integration with Amber and FEMU, and explores the accuracy–efficiency tradeoff.

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" chapters (9)

1. 0:00Intro
2. 0:20In a nutshell
3. 0:55Current art
4. 1:31Our approach
5. 1:42Challenges
6. 2:28System overview
7. 3:26Prior work
8. 3:48Evaluations
9. 4:35Future work

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