Bibliographic record
Abstract
Byzantine fault tolerant consensus protocols are a crucial component in blockchain systems. \nTraditional BFT consensus protocols have poor scalability, and their performance \nis sensitive to the latency between their participants, which leads to low performance in a \ngeo-distributed deployment. RCanopus is a consensus protocol that aims to provide high \nthroughput and good scalability in a geo-distributed environment. It organizes participants \ninto a hierarchical structure that is topology-aware. We implemented an ordering \nservice for HyperLedger Fabric with RCanopus, and evaluated its performance on AWS. \nOur implementation uses SBFT internally to provide BFT consensus. Comparing to running \nSBFT across all datacenters, RCanopus is able to achieve a 10.7x increase in peak \nthroughput in a deployment across 4 AWS regions. During our evaluation, we identi ed \nseveral design limitations that may a ect its performance or safety property. Therefore, we \nproposed five protocol extensions to further improve RCanopus in various aspects. This \nincludes handling stragglers, handling failures of entire Byzantine Groups, reducing the \nbandwidth usage and removing duplicated transactions. We implemented a prototype in \norder to evaluate the extensions that are not included in our ordering service. Our evaluation \nresults show that the new extensions can improve the peak throughput by 12.7% to \n114.3%, depending on the available bandwidth on wide area links.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".