作者：Liping Tao, Yang Lu, Yuqi Fan, Lei Shi, Chee Wei Tan, Zhen Wei

发表刊物：IEEE Transactions on Computational Social Systems

年份：June 2024

摘要：Sharding is a promising strategy to enhance blockchain scalability. However, the surge in transactions has led to heightened relations between nodes in the system, reflecting the volume of transactions between them. The increase in related nodes engaging in identical transactions across diverse shards leads to substantial cross-shard transactions, contributing to communication delays and impeding enhancements in throughput. Current methods typically employ greedy or heuristic approaches to organize nodes into shards, resulting in marginal reductions in the total relation between related nodes in different shards (i.e., the number of cross-shard transactions), while causing shard imbalance. Hence, there is a crucial need for periodic shard reorganization based on node relations to minimize the total relation between related nodes across different shards while ensuring shard balance. In this article, we investigate the reorganization of nodes into shards based on node relations in sharding blockchains, aiming to minimize the total relation between related nodes in different shards.We formulate the shard reorganization problem and introduce the shard reorganization algorithm based on the relation between nodes (SRRN) to address this issue. Theoretical analysis proves that SRRN is a 2λ M -approximation algorithm, where λ=( r max / r min ), with M representing the number of shards, and r max and r min denoting the maximum and minimum nonzero relations between nodes, respectively. Simulation results demonstrate that SRRN outperforms baseline algorithms in terms of total relation, degree of relation reduction, differences in computing power between shards, cross-shard ratio, and throughput.

参考文献拷贝字段：Liping Tao, Yang Lu, Yuqi Fan, Lei Shi, Chee Wei Tan, Zhen Wei. Throughput-Scalable Shard Reorganization Tailored to Node Relations in Sharding Blockchain Networks[J]. IEEE Transactions on Computational Social Systems. DOI: https://doi.org/10.1109/TCSS.2024.3406769