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Jing Shi, Xiao-Ying Bai, Wen-Zheng Zhang, Pei-Lun Li, Kai-Dong Wu, Guo-Li Yang, Ming-Tao Zhang. Performance Modeling and Testing of DAG-based Distributed Ledger Systems[J]. Journal of Computer Science and Technology. DOI: 10.1007/s11390-025-4616-8
Citation: Jing Shi, Xiao-Ying Bai, Wen-Zheng Zhang, Pei-Lun Li, Kai-Dong Wu, Guo-Li Yang, Ming-Tao Zhang. Performance Modeling and Testing of DAG-based Distributed Ledger Systems[J]. Journal of Computer Science and Technology. DOI: 10.1007/s11390-025-4616-8

Performance Modeling and Testing of DAG-based Distributed Ledger Systems

Funds: This work was supported by the National Key Research and Development Program of China (2022YFB2702300), the Special Project for Key Technology Tackling in Blockchain under the Science and Technology Innovation Action Plan in Shanghai (23511100300), and the National Natural Science Foundation of China (NSFC 72201275).
More Information
  • Received Date: July 28, 2024
  • Revised Date: January 13, 2025
  • Accepted Date: March 03, 2025
  • Performance is a major concern of the large-scale application of DLS (distributed ledger system). Compared with chain-based DLSs, DAG (directed acyclic graph) 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 TPS (transactions per second) 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.
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