BlockCompass: A benchmarking platform for blockchain performance
Bibliographic record
Abstract
Blockchain technology has gained momentum among researchers and other stakeholders due to its immutability and transparency. Several blockchain platforms with different kinds of consensus protocols have been proposed. However, this makes choosing and configuring such a platform, a non-trivial task. Several benchmarking tools have been presented to test the performance of blockchain solutions. However, these solutions are either limited to specific blockchain platforms or require complex configurations. Moreover, they tend to focus on transactional evaluation models, which may be counter-intuitive for longer-running instances under continuous workloads. In this work, we present BlockCompass, an all-inclusive blockchain benchmarking tool that can be easily configured and extended. We demonstrate how BlockCompass can evaluate the performance of a variety of blockchain platforms and configurations, including Ethereum Proof-of-Authority, Ethereum Proof-of-Work, Hyperledger Fabric Raft, Hyperledger Sawtooth with Proof-of-Elapsed-Time, Practical Byzantine Fault Tolerance and Raft consensus algorithms against workloads that continuously fluctuate over time. We also present the results of a usability study about the convenience and facility offered by BlockCompass in blockchain benchmarking.Â
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".