On Optimal Concurrency Control for Optimistic Replication
Bibliographic record
Abstract
Concurrency control is a core component in optimistic replication systems. To detect concurrent updates, the system associates each replicated object with metadata, such as, version vectors or causal graphs exchanged on synchronization opportunities. However, the size of such metadata increases at least linearly with the number of active sites. With recent trends in cloud computing, multi-regional collaboration, and mobile networks, the number of sites within a single replication system becomes very large. This imposes substantial overhead in communication and computation on every site. In this paper, we first present three version vector implementations that significantly reduce the cost of vector exchange by incrementally transferring vector elements. Basic rotating vectors (BRV) support systems providing no conflict reconciliation, whereas conflict rotating vectors (CRV) extend BRV to overcome this limitation. Skip rotating vectors (SRV) based on CRV further reduce data transmission. We show that both BRV and SRV are optimal implementations of version vectors, which, in turn, have minimal storage complexity among all known concurrency control schemes for state-transfer systems. We then present a causal graph exchange algorithm for operation-transfer systems with optimal communication overhead. All these algorithms adopt network pipelining to reduce running time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".