Predicting Deduplication Performance: An Analytical Model and Empirical Evaluation
Bibliographic record
Abstract
Deduplication is a technique to find and eliminate redundant blocks of data for efficient data backups, efficient versioning, reduced data transfers, and reduced data-storage overheads. For large datasets, especially with incremental updates over time (e.g., instrumentation data) and subsetting (e.g., for auxiliary experiments), deduplication makes data management faster and more efficient. The primary parameter of deduplication systems is the expected chunk size, and while many existing systems use accepted default values (e.g., 4 KB or 8 KB chunks), our experiments find that these values are suboptimal for finding duplicate data. Suboptimal deduplication and data management makes it harder for researchers to manipulate, share, and experiment with large datasets.We present the design, implementation, and an empirical validation of our analytical model that predicts the performance of deduplication parameters (i.e., ability to find duplicate data) on any given dataset. The empirical evaluation includes workloads based on source code (i.e., Linux kernel, Kubernetes, TensorFlow), an open-research dataset (i.e., CORD-19), and Wikipedia. Our experiments show that our model finds deduplication parameters that reduce the storage requirements by up to an additional 30.72% compared to a commonly used baseline. Our model is up to 19.8x faster than scanning, and the resulting deduplicated datasets are all within 5.14% of the deduped sizes found via the scan-based search.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".