Achieving Load Balancing in Blockchain Sharding Based on Multi-Objective Optimization

Yong Wang, Youquan Xian, Xueying Zeng, Dongcheng Li, Zhengdong Hu, Peng Liu

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.