Reversible conflict-free replicated data types
Bibliographic record
Abstract
Conflict-free replicated data types (CRDTs) are popular for optimistic replication and ensuring strong eventual consistency (SEC) in distributed systems. However, reversibility is an underdeveloped functionality for CRDTs, despite its usefulness in system restoration from an erroneous state or undoing unwanted operations. In this paper, we define the concept and design of reversible CRDTs (rCRDTs). Reverse operations compensate for the effect of reversed updates, and they extend existing CRDT interfaces. Three abstractions for reversibility are proposed: reversing a single update, multiple causally related updates, and multiple logically related updates that capture the user intention behind the updates. Moreover, a replicated and distributed key-value store, rKVCRDT, is implemented as a proof of concept that integrates the support of reversible CRDTs. The rCRDTs' evaluation show that although adding reversibility affects the system's performance, the end result depends on multiple factors and varies based on the underlying CRDTs. System designers must consider the trade-off between the benefit of reversibility and the performance impact.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".