Utilizing Different Predictive Methods to Optimize Hyperledger Fabric Instance
Bibliographic record
Abstract
This paper explores the use of reinforcement learn-ing and various machine learning techniques to optimize the configurations of Hyperledger Fabric v2 Channels and Orderers. Our goal is to increase the average throughput and success rate. To achieve this, we train multiple optimization agents using different algorithms, such as Q-learning and Deep Q Network. These agents interact with a Hyperledger Fabric v2 instance in a single host environment, using FabCar as the smart contract and Hyperledger Caliper as the benchmark platform.Based on the data collected from our reinforcement learning agents, we observed that the agents tend to choose larger batch sizes to optimize their results. Our training data also suggests that it is possible to achieve an average throughput of more than 98% for send rates up to 200 TPS. Our prediction results demonstrate that, even with the limitation of training time, reinforcement learning agents can increase the average throughput by around 59% and the average success rate by around 18%, while machine learning models can increase the average throughput by around 45% and the average success rate by around 8%, compared to the baseline results provided by the default configuration.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".