Optimizing Deduplication Parameters via a Change-Estimation Analytical Model
Bibliographic record
Abstract
Variable-sized, content-defined deduplication is a technique to find and eliminate redundant chunks of data for efficient data backups, reduced data transfers, and reduced data-storage overheads. For big datasets, especially with incremental updates over time such as backups and gathered data, deduplication makes data management faster and more efficient. While many existing deduplication systems use default expected chunk lengths such as 4 KB or 8 KB, they are suboptimal. Poorly optimized deduplication systems can significantly increase storage costs and network usage, making large datasets prohibitively expensive to manage. We present the design, implementation, and an empirical validation of our Deduplication Change-Estimation Analytical Model (DCAM) which predicts the performance of sliding window-based deduplication parameters on any given dataset, to be used for parameter optimization. Our empirical evaluation includes workloads based on source code (Linux kernel, Kubernetes, TensorFlow), open-research datasets (CORD-19), and articles (Wikipedia). Validated using both our system and the Destor deduplication system, a DCAM-based search finds deduplication parameters that require up to 3.8× less storage relative to a common baseline. DCAM Search optimizes parameters up to 19.8× faster than previously possible, and the size of the resulting deduplicated datasets are all within 5.15% of the best results found by searching using actual deduplication.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".