A Lightweight Locally Repairable Code-based Storage Architecture for Blockchains

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Wanning Bao, Liangmin Wang, Jie Chen
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引用次数: 0

Abstract

The blockchain system requires every node to preserve a complete copy of data arbitrarily, which exerts tremendous storage pressure on nodes. Some researchers applied the erasure code to reduce storage redundancy. However, code storage schemes have the problem of inefficient data communication while verifying transactions and downloading data. To solve this problem, this paper proposes a lightweight locally repairable code (LRC) storage scheme inspired by the idea of slice strategy from privacy computing. Firstly, partitioning each block into distinct transaction slices substantially reduces the amount of transmitted data required to verify a transaction. Secondly, our scheme can recover single-point data with fewer code data slices by local nodes and with less network communication overhead. At last, we analyze the performance of our scheme from theoretical perspectives and examine the storage performance and computation efficiency of our scheme from experimental perspectives. Results suggest that our scheme can effectively reduce the storage overhead while also decreasing the network communication overhead and improving the data reading efficiency.
一个轻量级的本地可修复的基于代码的区块链存储架构
区块链系统要求每个节点任意保留一份完整的数据副本,这给节点带来了巨大的存储压力。一些研究人员使用擦除码来减少存储冗余。然而,代码存储方案在验证事务和下载数据时存在数据通信效率低下的问题。为了解决这一问题,本文借鉴隐私计算中的切片策略思想,提出了一种轻量级的局部可修复代码(LRC)存储方案。首先,将每个块划分为不同的事务片大大减少了验证事务所需的传输数据量。其次,我们的方案可以用更少的本地节点代码数据切片和更少的网络通信开销来恢复单点数据。最后,从理论角度分析了该方案的性能,并从实验角度检验了该方案的存储性能和计算效率。结果表明,该方案可以有效降低存储开销,同时降低网络通信开销,提高数据读取效率。
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
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
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