MoltDB: Accelerating Blockchain via Ancient State Segregation

IF 5.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-10-10 DOI:10.1109/TPDS.2024.3467927

Junyuan Liang;Wuhui Chen;Zicong Hong;Haogang Zhu;Wangjie Qiu;Zibin Zheng

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引用次数: 0

Abstract

Blockchain store states in Log-Structured Merge (LSM) tree-based database. Due to blockchain traceability, the growing ancient states are inevitably stored in the databases. Unfortunately, by default, this process mixes current and ancient states in the data layout, increasing unnecessary disk I/O access and slowing transaction execution. This paper proposes MoltDB, a scalable LSM-based database for efficient transaction execution through a novel idea of ancient state segregation , i.e., to segregate current and ancient states in the data layout. However, the frequently generated and uncertainly accessed characteristics of ancient states make the segregation challenging. Thus, we develop an “extract-compact” mechanism to batch extraction process for frequently generated ancient states and the LSM compaction process to relieve additional disk I/O overhead. Moreover, we design an adaptive LSM-based storage for the uncertainly accessed ancient states extracted for on-demand access. We implement MoltDB as a database engine compatible with many mainstream blockchains and integrate it into Ethereum for evaluation. Experimental results show that MoltDB achieves 1.3 × transaction throughput and 30% disk I/O latency savings over the state-of-the-art works.

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MoltDB：通过古代状态隔离加速区块链发展

区块链将状态存储在基于日志结构合并（LSM）的树型数据库中。由于区块链的可追溯性，不断增长的古代状态不可避免地会存储在数据库中。不幸的是，默认情况下，这一过程会在数据布局中混合当前状态和古代状态，增加不必要的磁盘 I/O 访问，并减慢事务执行速度。本文提出了一种基于 LSM 的可扩展数据库 MoltDB，通过新颖的古态隔离思想（即在数据布局中隔离当前状态和古态）实现高效的事务执行。然而，由于古状态具有生成频繁、访问不确定的特点，因此隔离工作具有挑战性。因此，我们开发了一种 "提取-压缩 "机制，对频繁生成的古状态和 LSM 压缩过程进行批量提取，以减轻额外的磁盘 I/O 开销。此外，我们还设计了一种基于 LSM 的自适应存储，用于按需访问提取的不确定访问古状态。我们将 MoltDB 实现为与许多主流区块链兼容的数据库引擎，并将其集成到以太坊中进行评估。实验结果表明，MoltDB 实现了 1.3 倍的交易吞吐量，磁盘 I/O 延迟比最先进的作品节省了 30%。

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来源期刊

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.