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Dynamic-EC: an efficient dynamic erasure coding method for permissioned blockchain systems

delete2024-11-13
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AI
M
Mizhipeng Zhang
吴
吴晨涛 (Chentao Wu) *
李洁 封面图
李洁 (Jie Li)
过
过敏意 (Minyi Guo)
DOI:10.1007/s11704-023-3209-3delete
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摘要

摘要

En 中文
Blockchain as a decentralized storage technology is widely used in many fields. It has extremely strict requirements for reliability because there are many potentially malicious nodes. Generally, blockchain is a chain storage structure formed by interconnecting blocks, which are stored by full replication method, where each node stores a replica of all blocks and the data consistency is maintained by the consensus protocol. To decrease the storage overhead, previous approaches such as BFT-Store and Partition Chain store blocks via erasure codes. However, existing erasure coding based methods utilize static encoding schema to tolerant f malicious nodes, but in the typical cases, the number of malicious nodes is much smaller than f as described in previous literatures. Using redundant parities to tolerate excessive malicious nodes introduces unnecessary storage overhead.To solve the above problem, we propose Dynamic-EC, which is a Dynamic Erasure Coding method in permissioned blockchain systems. The key idea of Dynamic-EC is to reduce the storage overhead by dynamically adjusting the total number of parities according to the risk level of the whole system, which is determined by the number of perceived malicious nodes, while ensuring the system reliability. To demonstrate the effectiveness of Dynamic-EC, we conduct several experiments on an open source blockchain software Tendermint. The results show that, compared to the state-of-the-art erasure coding methods, Dynamic-EC reduces the storage overhead by up to 42%, and decreases the average write latency of blocks by up to 25%, respectively.
Keyword:
blockchain
Byzantine Fault Tolerance (BFT)
erasure coding
consensus
reputation evaluation

期刊

Frontiers of Computer Science 封面图
Frontiers of Computer Science
IF:
4.6
论文数:
1.6K
被引数:
2.8K

机构

S
shanghai jiao tong university
学者数:
15.7W
论文数: 11.7W
被引数: 159
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