Sparrow:通过分片间缓存加速区块链分片的智能合约执行

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-12-26 DOI:10.1109/TPDS.2024.3522016
Junyuan Liang;Peiyuan Yao;Wuhui Chen;Zicong Hong;Jianting Zhang;Ting Cai;Min Sun;Zibin Zheng
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引用次数: 0

摘要

分片是一种很有前途的扩展区块链的解决方案,它将系统分成多个分片来并行处理事务。然而,由于状态分离和分片隔离,在区块链分片系统上有效支持智能合约仍然具有挑战性,在区块链分片系统中,智能合约可以相互交互,涉及多个分片维护的状态。具体来说,现有的分片系统采用昂贵的多步协作机制来执行智能合约,导致延迟长,吞吐量低。本文提出了一种区块链分片协议Sparrow,它实现了智能合约的一步执行。为了打破分片隔离,受传统数据库非本地热点数据缓存的启发,我们提出了一种分片间缓存的新思路,允许一个分片预取和缓存其他分片频繁访问的合约状态。因此,矿工可以使用分片间缓存来预执行待处理事务,检索其所有合约调用,并在一步中将其提交到多个分片。特别是,我们首先提出了一种推测式分散缓存同步机制,用于在拜占庭环境中跨分片进行高效和安全的缓存同步。然后,我们提出了一种多分支探索机制来解决依赖关系契约调用的乐观一步执行过程中的回滚问题。我们还提出了一系列冲突解决机制,以减少由固有事务冲突引起的回滚。我们实现了Sparrow和现有分片系统的原型,评估表明,与现有分片系统相比,Sparrow的吞吐量提高了2.44倍,交易延迟减少了30%。
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Sparrow: Expediting Smart Contract Execution for Blockchain Sharding via Inter-Shard Caching
Sharding is a promising solution to scale blockchain by separating the system into multiple shards to process transactions in parallel. However, due to state separation and shard isolation, it is still challenging to efficiently support smart contracts on a blockchain sharding system where smart contracts can interact with each other, involving states maintained by multiple shards. Specifically, existing sharding systems adopt a costly multi-step collaboration mechanism to execute smart contracts, resulting in long latency and low throughput. This article proposes Sparrow, a blockchain sharding protocol achieving one-step execution for smart contracts. To break shard isolation, inspired by non-local hotspot data caching in traditional databases, we propose a new idea of inter-shard caching, allowing a shard to prefetch and cache frequently accessed contract states of other shards. The miner can thus use the inter-shard cache to pre-execute a pending transaction, retrieve all its contract invocations, and commit it to multiple shards in one step. Particularly, we first propose a speculative dispersal cache synchronisation mechanism for efficient and secure cache synchronization across shards in Byzantine environments. Then, we propose a multi-branch exploration mechanism to solve the rollback problem during the optimistic one-step execution of contract invocations with dependencies. We also present a series of conflict resolution mechanisms to decrease the rollback caused by inherent transaction conflicts. We implement prototypes for Sparrow and existing sharding systems, and the evaluation shows that Sparrow improves the throughput by $2.44\times$ and reduces the transaction latency by 30% compared with the existing sharding systems.
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
期刊最新文献
2024 Reviewers List* HpT: Hybrid Acceleration of Spatio-Temporal Attention Model Training on Heterogeneous Manycore Architectures Sparrow: Expediting Smart Contract Execution for Blockchain Sharding via Inter-Shard Caching CAT: Cellular Automata on Tensor Cores UMPIPE: Unequal Microbatches-Based Pipeline Parallelism for Deep Neural Network Training
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