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arXiv 2011.13837cs.CR

区块链中交易并行性理论

A theory of transaction parallelism in blockchains

Massimo Bartoletti, Letterio Galletta, Maurizio Murgia

AI总结:

针对区块链交易串行执行限制吞吐量的问题,提出基于静态分析的交易并行性理论,使节点并行执行交易,并在以太坊上初步验证可提升性能。

摘要:

去中心化的区块链平台使得加密资产的安全交换无需可信权威的中介即可实现。为此,这些平台依赖于一个由拜占庭节点组成的点对点网络,这些节点协作维护一个仅追加的交易账本,称为区块链。交易代表用户所需的行为,例如将一定数量的加密货币单位转移给另一个用户,或执行一个根据其内部逻辑分配加密资产的智能合约。点对点网络中的部分节点竞争将交易追加到区块链上。为此,它们将用户发送的交易分组为区块,并按所选顺序执行这些交易来更新其对区块链状态的视图。一旦一个交易区块被追加到区块链上,其他节点会验证它,并按相同顺序重新执行交易。交易的串行执行未能利用现代处理器的多核架构,从而限制了吞吐量。在本文中,我们提出了一种基于交易和智能合约静态分析的区块链交易并行性理论。我们展示了区块链节点如何使用我们的理论来并行化交易的执行。在以太坊上的初步实验表明,我们的技术可以提高节点的性能。

英文摘要:

Decentralized blockchain platforms have enabled the secure exchange of crypto-assets without the intermediation of trusted authorities. To this purpose, these platforms rely on a peer-to-peer network of byzantine nodes, which collaboratively maintain an append-only ledger of transactions, called blockchain. Transactions represent the actions required by users, e.g. the transfer of some units of crypto-currency to another user, or the execution of a smart contract which distributes crypto-assets according to its internal logic. Part of the nodes of the peer-to-peer network compete to append transactions to the blockchain. To do so, they group the transactions sent by users into blocks, and update their view of the blockchain state by executing these transactions in the chosen order. Once a block of transactions is appended to the blockchain, the other nodes validate it, re-executing the transactions in the same order. The serial execution of transactions does not take advantage of the multi-core architecture of modern processors, so contributing to limit the throughput. In this paper we develop a theory of transaction parallelism for blockchains, which is based on static analysis of transactions and smart contracts. We illustrate how blockchain nodes can use our theory to parallelize the execution of transactions. Initial experiments on Ethereum show that our technique can improve the performance of nodes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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