基于IOTA dag的分布式账本性能分析

IF 0.7 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Modeling and Performance Evaluation of Computing Systems Pub Date : 2021-09-01 DOI:10.7939/R3-W1C1-WT05
Caixiang Fan, Sara Ghaemi, Hamzeh Khazaei, Yuxiang Chen, P. Musílek
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引用次数: 5

摘要

分布式账本(dl)在物联网项目中提供了许多优于集中式解决方案的优势,包括但不限于提高安全性、透明度和容错性。为了大规模地利用dl,应该充分分析和处理它们众所周知的限制(即性能)。为了解决性能和可伸缩性问题,提出了基于有向无环图的分布式数据库。其中第一个是IOTA,在解决上述问题方面显示出有希望的迹象。IOTA是一个为物联网设计的开源DL。它使用有向无环图在其分类账上存储交易,以实现比基于区块链的dl更高的可扩展性。然而,由于部署共识的不确定性和集中化,目前的IOTA实现暴露了一些性能问题,使其性能低于最初的设计。在本文中,我们首先扩展了现有的模拟器以支持现实的IOTA模拟,并研究了不同设计参数对IOTA性能的影响。然后,我们提出了一个分层模型,以帮助IOTA用户确定重新发送先前提交但尚未确认的交易的最佳等待时间。我们的研究结果揭示了交易到达率、tip选择算法、加权tip选择算法随机性和网络延迟对吞吐量的影响。使用提出的分层模型,我们对确认交易的分布有了一些了解。利用该分布计算将未经确认的事务重新发送到DL的最佳时间。系统设计人员和用户都可以使用性能分析结果来支持他们的决策。
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Performance Analysis of the IOTA DAG-Based Distributed Ledger
Distributed ledgers (DLs) provide many advantages over centralized solutions in Internet of Things projects, including but not limited to improved security, transparency, and fault tolerance. To leverage DLs at scale, their well-known limitation (i.e., performance) should be adequately analyzed and addressed. Directed acyclic graph-based DLs have been proposed to tackle the performance and scalability issues by design. The first among them, IOTA, has shown promising signs in addressing the preceding issues. IOTA is an open source DL designed for the Internet of Things. It uses a directed acyclic graph to store transactions on its ledger, to achieve a potentially higher scalability over blockchain-based DLs. However, due to the uncertainty and centralization of the deployed consensus, the current IOTA implementation exposes some performance issues, making it less performant than the initial design. In this article, we first extend an existing simulator to support realistic IOTA simulations and investigate the impact of different design parameters on IOTA’s performance. Then, we propose a layered model to help the users of IOTA determine the optimal waiting time to resend the previously submitted but not yet confirmed transaction. Our findings reveal the impact of the transaction arrival rate, tip selection algorithms, weighted tip selection algorithm randomness, and network delay on the throughput. Using the proposed layered model, we shed some light on the distribution of the confirmed transactions. The distribution is leveraged to calculate the optimal time for resending an unconfirmed transaction to the DL. The performance analysis results can be used by both system designers and users to support their decision making.
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9
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