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Analysis of Dynamic Transaction Fee Blockchain Using Queueing Theory

Author

Listed:
  • Koki Inami

    (Graduate School of Science and Technology, University of Tsukuba, Tsukuba 305-8573, Japan
    These authors contributed equally to this work.)

  • Tuan Phung-Duc

    (Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba 305-8573, Japan
    These authors contributed equally to this work.)

Abstract

In recent years, blockchains have been attracting attention because they are decentralized networks with transparency and trustworthiness. Generally, transactions on blockchain networks with higher transaction fees are processed preferentially compared to others. The processing fee varies significantly depending on other transactions; it is difficult to predict the fee, and it may be significantly high. These are major barriers to blockchain utilization. Although several consensus algorithms have been proposed to solve these problems, their performance has not been fully evaluated. In this study, we model a blockchain system with a base fee, such as in Ethereum, via a priority queueing model. To assess the model’s performance, we derive the stability condition, stationary probability, average number of customers, and average waiting time for each type of customer. In deriving the stability conditions, we propose a method that uses the theoretical values of the partial models. These theoretical values match well with those obtained from Monte Carlo simulations, confirming the validity of the analysis.

Suggested Citation

  • Koki Inami & Tuan Phung-Duc, 2025. "Analysis of Dynamic Transaction Fee Blockchain Using Queueing Theory," Mathematics, MDPI, vol. 13(6), pages 1-18, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:6:p:1010-:d:1616589
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    References listed on IDEAS

    as
    1. Easley, David & O'Hara, Maureen & Basu, Soumya, 2019. "From mining to markets: The evolution of bitcoin transaction fees," Journal of Financial Economics, Elsevier, vol. 134(1), pages 91-109.
    2. Ronald W. Wolff, 1982. "Poisson Arrivals See Time Averages," Operations Research, INFORMS, vol. 30(2), pages 223-231, April.
    3. Conall Butler & Martin Crane, 2023. "Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods," Mathematics, MDPI, vol. 11(9), pages 1-26, May.
    Full references (including those not matched with items on IDEAS)

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