Research on transaction allocation strategy in blockchain state sharding

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-15 DOI:10.1016/j.future.2025.107756

Guangxia Xu , Zhean Zhou , Xiaoling Song , Yongfei Huang

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引用次数: 0

Abstract

With the continuous enrichment of blockchain application scenarios, people have higher requirements for blockchain throughput and storage costs. State sharding is one of the most promising technologies for blockchain. It decentralizes the storage of the blockchain ledger to effectively reduce storage costs while increasing the throughput of the blockchain. However, it still has the hot sharding problem of most transactions in individual committees. This paper proposes a sharding transaction allocation strategy (STAS) to score committees and transactions according to different methods and assign high-scoring transactions to high-scoring committees. This allocation strategy, which allocates transactions on demand based on node capacity, mitigates hot sharding issues and makes it safer to hand over more valuable transactions to a more honest committee. Comparative experiments show that the proposed STAS strategy has lower latency and higher throughput than the previous sharding model.

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区块链状态分片中事务分配策略研究

随着区块链应用场景的不断丰富，人们对区块链吞吐量和存储成本提出了更高的要求。状态分片是区块链最有前途的技术之一。它将区块链账本的存储去中心化，在提高区块链吞吐量的同时有效降低了存储成本。然而，它仍然存在大多数事务在单个委员会中的热分片问题。本文提出了一种分片交易分配策略（STAS），根据不同的方法对委员会和交易进行打分，并将高分交易分配给高分委员会。这种分配策略根据节点容量按需分配交易，缓解了热分片问题，并使将更有价值的交易移交给更诚实的委员会更安全。对比实验表明，与之前的分片模型相比，本文提出的STAS策略具有更低的延迟和更高的吞吐量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.