Performance Modeling and Testing of DAG-Based Distributed Ledger Systems.
Saved in:
| Title: | Performance Modeling and Testing of DAG-Based Distributed Ledger Systems. |
|---|---|
| Authors: | Shi, Jing1 (AUTHOR) jingshi@stu.pku.edu.cn, Bai, Xiao-Ying2 (AUTHOR) baixy@aibd.ac.cn, Zhang, Wen-Zheng1 (AUTHOR) zwz@stu.pku.edu.cn, Li, Pei-Lun3 (AUTHOR) peilun.li@confluxnetwork.org, Wu, Kai-Dong1 (AUTHOR) wukd94@pku.edu.cn, Yang, Guo-Li2 (AUTHOR) yanggl@aibd.ac.cn, Zhang, Ming-Tao1 (AUTHOR) mingtaozhang@pku.edu.cn |
| Source: | Journal of Computer Science & Technology (10009000). Nov2025, Vol. 40 Issue 6, p1593-1607. 15p. |
| Subjects: | Directed graphs, Transaction systems (Computer systems), Scalability, Automation software, Information technology, Benchmark problems (Computer science), Blockchains |
| Abstract: | Performance is a major concern of the large-scale application of distributed ledger systems (DLSs). Compared with chain-based DLSs, directed acyclic graph (DAG)-based DLSs are promising to enhance transaction parallel processing capabilities greatly and have gained increasing interest. However, due to the complex technology stack, current metrics, such as transactions per second (TPS) and latency, are insufficient for a deep understanding of DAG-based DLS performance. To address this problem, based on a comprehensive analysis of the transaction lifecycle process, we propose a state model and a set of performance indicators by identifying the key operations in the workflow. Then we develop an automated testing tool and conduct experiments on two representative open-source systems, IOTA and Conflux, considering their open-source nature, extensive documentation, and representativeness. The experiments profile the DAG-based DLS performance with respect to the system architecture, DAG topology, runtime behavior, and DAG processing mechanisms. The state model, indicators, and key experiment findings are valuable for future DLS design, deployment, and performance optimization. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Computer Science & Technology (10009000) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!