Block and Transaction Delivery in Ethereum Network
Bibliographic record
Abstract
We present a comprehensive analytical model for block and transaction distribution in the Ethereum P2P network. We model the data distribution protocol in which a node forwards a full block (transaction) to some of its peers and its hash to others, and combine this model with the connectivity and transmission models to obtain input and output data rates, which are then fed into a priority M/G/1 Jackson network queuing system in which blocks are given preference over transactions, and transactions are further grouped into two priority classes according to gasprice. Block and transaction delivery times are found to be mainly determined by node connectivity and network size, and prioritization provides faster service for higher priority transactions. We also model an Eclipse-like attack that degrades data delivery times and block finalization time, i.e., the time for a block to be officially confirmed, by reducing network connectivity, and show that its impact can be countered by adjusting the portion of peers which receive a full block. Lastly, we determine the probability of uncle blocks being included in the longer chain and demonstrate how the Eclipse attack affects this probability.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".