Performance Evaluation of E-Voting Based on Hyperledger Fabric Blockchain Platform
Bibliographic record
Abstract
Permissioned blockchain platforms have become more prevalent in a wide range of applications. These, such as hyperledger fabric platforms, are sensitive to latency and throughput. In this work, the E-voting case study adopts a hyperledger fabric platform where performance evaluation has been studied in terms of scalability, latency, throughput, CPU usage, and memory allocation. Three scenarios were performed with varying transaction rates, block size, and organizations. Another two scenarios were performed, first with varying block timeout and second, measuring the impact of CPUs and memory allocation on the proposed fabric’s entities (peers, orderer, couchDB, chaincode, etc.). The result shows that an increase in block size will significantly affect metrics such as latency and throughput. Good results were obtained with high transaction send rates on large block size. Similarly, low performance is obtained using a small block size with increased send rates. Also, it was noticed that increasing the number of organizations will increase latency and decrease the throughput. Therefore, in applications with a large number of concurrent transactions, to maintain high throughput, block timeouts and block size should be large. On The other hand, the number of CPUs and amount of memory allocation would impact hyperledger fabric performance.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".