Achieving Load Balancing in Blockchain Sharding Based on Multi-Objective Optimization
Bibliographic record
Abstract
Blockchain is distinguished by its decentralization and security, but it faces significant scalability challenges. Sharding is widely regarded as a key solution for improving blockchain scalability. However, existing sharding schemes, such as Monoxide, often suffer from load imbalance and excessive cross-shard transactions (TXs), which degrades system performance. To address these issues, we propose Sharding-aware NSGA-II (SNSGA-II), a variant of the Non-dominated Sorting Genetic Algorithm II, designed to optimize account allocation in blockchain sharding. We formulate the allocation problem as a multi-objective optimization task and develop a SNSGA-II-based sharding algorithm that dynamically adjusts account allocation to improve load balancing while minimizing cross-shard TXs. The proposed approach achieves a well-balanced trade-off in terms of Pareto efficiency, ensuring that improvements in load balancing do not come at the cost of excessive cross-shard TXs. We evaluate our method through simulation experiments on a real Ethereum dataset. The results demonstrate that our method significantly outperforms existing solutions, including Metis, Monoxide, and HyperChain, in key metrics such as throughput and TX confirmation delay.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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".