Learned Sharding Toward Sustainable Communications and Networking in Blockchains

IF 5.3 2区 计算机科学 Q1 TELECOMMUNICATIONS
Bo Yin;Rongyao Rong;Xiaoli Xiao
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Abstract

Sharding scales blockchain by grouping blockchain nodes into committees each of which processes a portion of the total transactions in parallel. The issue with sharding is the enormous volume of cross-shard transactions, which results in high communication costs to ensure transaction atomicity. The account-based sharding problem can be viewed as the vertex classification problem of the account-transaction graph. However, prior studies employed traditional graph partitioning algorithms for sharding, failing to make full use of the account relationship in the graph structure. In this work, we aim to address the sharding problem from the perspective of deep learning that can learn the graph structure toward sustainable communications. We propose an efficient deep learning-based sharding scheme (DLS) based on the graph attention (GAT) network. The account and transaction information are input into the GAT for semi-supervised training and account/vertex classification. Since the performance may degrade in the case of limited label information, we incorporate the label propagation method to acquire the label information of non-trained accounts. We also extend our approach to deal with the new account scenario without retraining the neural network. Extensive experiments on Ethereum data demonstrate that our proposed DLS can effectively reduce the number of cross-shard transactions.
学习分片,实现区块链的可持续通信和联网
分片通过将区块链节点分组为委员会来扩展区块链,每个委员会并行处理总交易量的一部分。分片的问题在于跨分片交易量巨大,这导致确保交易原子性的通信成本很高。基于账户的分片问题可视为账户交易图的顶点分类问题。然而,之前的研究采用传统的图分割算法进行分片,未能充分利用图结构中的账户关系。在这项工作中,我们旨在从深度学习的角度解决分片问题,从而学习图结构,实现可持续通信。我们基于图注意力(GAT)网络提出了一种基于深度学习的高效分片方案(DLS)。账户和交易信息被输入到 GAT 中进行半监督训练和账户/顶点分类。由于在标签信息有限的情况下性能可能会下降,我们采用了标签传播方法来获取非训练账户的标签信息。我们还扩展了我们的方法,以处理新账户情况,而无需重新训练神经网络。在以太坊数据上进行的大量实验证明,我们提出的 DLS 可以有效减少跨碎片交易的数量。
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
IEEE Transactions on Green Communications and Networking
IEEE Transactions on Green Communications and Networking Computer Science-Computer Networks and Communications
CiteScore
9.30
自引率
6.20%
发文量
181
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