Performance Modeling of Public Permissionless Blockchains: A Survey

IF 28 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2025-01-24 DOI:10.1145/3715094
Molud Esmaili, Ken Christensen
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引用次数: 0

Abstract

Public permissionless blockchains facilitate peer-to-peer digital transactions, yet face performance challenges, specifically minimizing transaction confirmation time to decrease energy and time consumption per transaction. Performance evaluation and prediction is crucial in achieving this objective, with performance modeling as a key solution despite the complexities involved in assessing these blockchains. This survey examines prior research concerning the systems used to model blockchain performance, specifically focusing on public permissionless blockchains. Initially, it provides foundational knowledge about these blockchains and the crucial performance parameters for their assessment. Additionally, the study delves into research on the performance modeling of public permissionless blockchains, predominantly considering these systems as bulk service queues. It also examines prior studies on workload and traffic modeling, characterization, and analysis within these blockchain networks. By analyzing existing research, our survey aims to provide insights and recommendations for researchers keen on enhancing the performance of public permissionless blockchains or devising novel mechanisms in this domain.
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公共无许可区块链的性能建模:调查
公共无许可区块链促进了点对点数字交易,但面临性能挑战,特别是最大限度地减少交易确认时间,以减少每笔交易的能源和时间消耗。性能评估和预测对于实现这一目标至关重要,尽管评估这些区块链涉及复杂性,但性能建模是关键的解决方案。本调查检查了先前关于用于模拟区块链性能的系统的研究,特别关注公共无许可区块链。最初,它提供了关于这些区块链的基础知识和用于评估的关键性能参数。此外,该研究还深入研究了公共无权限区块链的性能建模,主要将这些系统视为批量服务队列。它还检查了这些区块链网络中关于工作负载和流量建模、表征和分析的先前研究。通过分析现有研究,我们的调查旨在为热衷于提高公共无许可区块链性能或在该领域设计新机制的研究人员提供见解和建议。
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
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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