Bibliographic record
Abstract
All-to-all data transmission is a typical data transmission pattern in both consensus protocols and blockchain systems. Developing an optimization scheme that provides high throughput and low latency data transmission can significantly benefit the performance of those systems. This paper investigates the problem of optimizing all-to-all data transmission in a wide area network (WAN) using overlay multicast. We prove that in a hose network model, using shallow tree overlays with height up to two is sufficient for all-to-all data transmission to achieve the optimal throughput allowed by the available network resources. Upon this foundation, we build ShallowForest, a data plane optimization for consensus protocols and blockchain systems. The goal of ShallowForest is to improve consensus protocols' resilience to skewed client load distribution. Experiments with skewed client load across replicas in the Amazon cloud demonstrate that ShallowForest can improve the commit throughput of the EPaxos consensus protocol by up to 100% with up to 60% reduction in commit latency.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.002 |
| 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".