Enhancing Throughput in Hyperledger Fabric Through Endorsement Policy Strategy
Bibliographic record
Abstract
In the realm of private permissioned blockchain platforms, increasing throughput is a pivotal objective. This paper focuses on the optimization of throughput in Hyperledger Fabric, a private permissioned leading blockchain framework tailored for enterprise applications. The paper proposes a novel approach to enhancing the platform’s performance by reevaluating the endorsement policy. By implementing a Less-Than-Half endorsement policy, the paper aims to streamline transaction validation processes and bridge the gap between Fabric’s throughput and the demands of large-scale industrial applications. The proposed method objects to boost transaction throughput without compromising security or reliability. The paper provides an overview of Hyperledger Fabric architecture, discusses the pre-verification mechanism, and presents the proposed method for optimizing throughput. The performance of the system is analyzed using Hyperledger Caliper and Prometheus. Simulation results show increase in the throughput of the Less-Than-Half of the endorsement policy as compared to the majority and it also demonstrates the significant reduction in the latency of the Less-Than-Half endorsement policy.
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 | 0.005 |
| 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 | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".