Flash reliability in production: the expected and the unexpected
Bibliographic record
Abstract
As solid state drives based on flash technology are becoming a staple for persistent data storage in data centers, it is important to understand their reliability characteristics. While there is a large body of work based on experiments with individual flash chips in a controlled lab environment under synthetic workloads, there is a dearth of information on their behavior in the field. This paper provides a large-scale field study covering many millions of drive days, ten different drive models, different flash technologies (MLC, eMLC, SLC) over 6 years of production use in Google's data centers. We study a wide range of reliability characteristics and come to a number of unexpected conclusions. For example, raw bit error rates (RBER) grow at a much slower rate with wearout than the exponential rate commonly assumed and, more importantly, they are not predictive of uncorrectable errors or other error modes. The widely used metric UBER (uncorrectable bit error rate) is not a meaningful metric, since we see no correlation between the number of reads and the number of uncorrectable errors. We see no evidence that higher-end SLC drives are more reliable than MLC drives within typical drive lifetimes. Comparing with traditional hard disk drives, flash drives have a significantly lower replacement rate in the field, however, they have a higher rate of uncorrectable errors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".