Bibliographic record
Abstract
Replication is a technique widely used in parallel and distributed systems to provide qualities such as performance, scalability, reliability and availability to their clients. These qualities comprise the non-functional requirements of the system. But the functional requirement consistency may also get affected as a side-effect of replication. Different replica control protocols provide different levels of consistency from the system. In this paper we present the middleware based McRep replication protocol that supports multiple consistency model in a distributed system with replicated data. Both correctness criteria and divergence aspects of a consistency model can be specified in the McRep configuration. Supported correctness criteria include linearizability, sequential consistency, serializability, snapshot isolation and causal consistency. Bounds on divergence can be specified in either version metric or delay metric. Our approach allows the same middleware to be used for applications requiring different consistency guarantees, eliminating the need for mastering a new replication middleware or framework for every application. We carried out experiments to compare the performance of various consistency requirements in terms of response time, concurrency conflict and bandwidth overhead. We demonstrate that in McRep workloads only pay for the consistency guarantees they actually need.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".