https://doi.org/10.62660/bcstu/4.2025.107

Volume 30, No. 4, 2025

107-118

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    • A strategy for adaptive quorum adjustment (AQA) to achieve deterministic consensus under variable latencies

    Olha Krasnozhon

    Received 29.05.2025, Revised 01.01.1970, Accepted 15.12.2025

    Abstract

    Reliability of replicated state machines under latency skew is undermined by nondeterministic leader elections and commit ordering, which complicates testing, bug reproduction, audits, and on-call recovery in real deployments. The study aimed to restore deterministic consensus under variable latencies by specifying Adaptive Quorum Adjustment (AQA). The methodology fixed observation-window and sensitivity parameters a priori and evaluated neutral exemplars (replicated log, in-memory register, parser-driven machine, Abstract Syntax Tree transformations) on 5- and 7-node clusters across near-normal, bimodal, heavy-tailed, bursty, and split-merge regimes. Across 12,000 election-commit rounds, AQA eliminated mismatches in both leader sequence and commit order (24,000 hash comparisons, 0%), reduced re-elections by 37.5-40.4% (mean –38.9%), and contracted long tail decision times (election p99 –24.8% on average; commit p99 –25.6%) while preserving safety via mandated quorum intersections (N = 5: qt ∈ [3, 5]; N = 7 : qt ∈ [4, 6]). Non-reproducibility – seen as leader-sequence and commit order mismatches, long-tail latencies, and unnecessary re-elections – stemmed from randomised timeouts and multivalued quorum sizing, whereas restored determinism is a structural consequence of stable node ranking, a total-order quorum rule, and guaranteed intersections of prefix quorums. Deterministic leader/commit histories make test runs and failure-injection scenarios replay-identical, shorten incident timelines by curbing election thrash and tail latencies, simplify post-mortems through stable event orderings, and improve operator confidence during partitions and healing; and because AQA is a strategy rather than an invention, it can be adopted openly as a guardrail around learning or adaptive modules without patent encumbrances

    Keywords:

    leader election; commit order; timing skew; node ranking; tie breaking; safety invariants; replicated lo

    Suggested citation
    Krasnozhon, О. (2025). A strategy for adaptive quorum adjustment (AQA) to achieve deterministic consensus under variable latencies. Bulletin of Cherkasy State Technological University, 30(4), 107-118. https://doi.org/10.62660/bcstu/4.2025.107
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    References

    1. Ahn, J., Yi, E., & Kim, M. (2024). Blockchain consensus mechanisms: A bibliometric analysis (2014-2024) using VOSviewer and R Bibliometrix. Information, 15(10), article number 644. doi: 10.3390/info15100644.
    2. Bai, F., Li, F., Shen, T., Zeng, K., Zhang, X., & Zhang, C. (2024). RaBFT: An improved Byzantine fault tolerance consensus algorithm based on raft. The Journal of Supercomputing, 80, 21533-21560. doi: 10.1007/s11227-024-06284-6.
    3. Battisti, J.H.F., Batista, V.E., Koslovski, G.P., Pillon, M.A., Miers, C.C., & Marques, M.A. (2023). Performance analysis of the Raft consensus algorithm on Hyperledger Fabric and Ethereum on cloud. In Proceedings of the international conference on cloud computing technology and science (pp. 155-160). Naples: IEEE. doi: 10.1109/ CloudCom59040.2023.00035.
    4. Bokhari, M.U., Kareem, A., & Hanafi, B. (2024). Exploring fault tolerance consensus for wireless sensor networks: A comprehensive detailed study. Journal of Electrical Systems, 20(7), 1653-1663. doi: 10.52783/jes.3752.
    5. Fahim, S., Rahman, S.M., & Mahmood, S. (2023). Blockchain: A comparative study of consensus algorithms PoW, PoS, PoA, PoV. International Journal of Mathematical Sciences and Computing, 9(3), 46-57. doi: 10.5815/ ijmsc.2023.03.04.
    6. Gol, D.A., & Gondaliya, N. (2024). Blockchain: A comparative analysis of hybrid consensus algorithm and performance evaluation. Computers & Electrical Engineering, 117(4), article number 108934. doi: 10.1016/j. compeleceng.2023.108934.
    7. Golpayegani, F., Chen, N., Afraz, N., Gyamfi, E., Malekjafarian, A., Schäfer, D., & Krupitzer, C. (2024). Adaptation in edge computing: A review on design principles and research challenges. ACM Transactions on Autonomous and Adaptive Systems, 19(3), article number 19. doi: 10.1145/3664200.
    8. Jin, B., Hu, Y., Tao, H., & He, Y. (2022). An improved practical Byzantine fault-tolerant consensus algorithm combined with aggregating signature. In Proceedings of the 7th international symposium on advances in electrical, electronics, and computer engineering (article number 122944S). Xishuangbanna: SPIE. doi: 10.1117/12.2639706.
    9. Lashkari, B., & Musilek, P. (2021). A comprehensive review of blockchain consensus mechanisms. IEEE Access, 9, 43620-43652. doi: 10.1109/ACCESS.2021.3065880.
    10. Li, D., & Hu, S. (2023). Adaptive consensus reaching process with dynamic weights and minimum adjustments for group interactive portfolio optimisation. Computers & Industrial Engineering, 183(4), article number 109491. doi: 10.1016/j.cie.2023.109491.
    11. Liu, J., Feng, W., Huang, M., Feng, S., & Zhang, Yu. (2023). Grouped multilayer practical Byzantine fault tolerance algorithm: A practical Byzantine fault tolerance consensus algorithm optimised for digital asset trading scenarios. Sensors, 23(21), article number 8903. doi: 10.3390/s23218903.
    12. Liu, S., Zhang, R., Liu, C., Xu, C., & Wang, J. (2023). An improved PBFT consensus algorithm based on grouping and credit grading. Scientific Reports, 13, article number 13030. doi: 10.1038/s41598-023-28856-x.
    13. Liu, X., & Zhu, J. (2024). An improved practical Byzantine fault tolerance algorithm for aggregating node preferences. Scientific Reports, 14(1), article number 31200. doi: 10.1038/s41598-024-82579-1.
    14. Luo, H., Yang, X., Yu, H., Sun, G., Lei, B., & Guizani, M. (2023). Performance analysis and comparison of non-ideal wireless PBFT and RAFT consensus networks in 6G communications. ArXiv. doi: 10.48550/arXiv.2304.08697.
    15. Meng, F., Chen, B., & Tan, C. (2022). Adaptive minimum adjustment consensus model for large-scale group decision making under social networks and its application in Integrated Care of Older People. Applied Soft Computing, 132(1), article number 109863. doi: 10.1016/j.asoc.2022.109863.
    16. Mughal, F.R., He, J., Das, B., Dharejo, F.A., Zhu, N., Khan, S.B., & Alzahrani, S. (2024). Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering. Scientific Reports, 14(1), article number 28746. doi: 10.1038/s41598-024-78239-z.
    17. Murthy, V.S.N., Kumari, R., Goyal, M., Dubey, P., Meenakshi, Manikandan, S., & Ramesh, P. (2025). Edge-AI in IoT: Leveraging cloud computing and big data for intelligent decision-making. Journal of Information Systems Engineering & Management, 10(20), 601-619. doi: 10.52783/jisem.v10i20s.3194.
    18. Piao, X., Li, M., Meng, F., & Song, H. (2022). Latency analysis for raft consensus on Hyperledger fabric. In D. Svetinovic, Y. Zhang, X. Luo, X. Huang & X. Chen (Eds.), Blockchain and trustworthy systems (pp. 165-176). Singapore: Springer. doi: 10.1007/978-981-19-8043-5_12.
    19. Qian, C., Tang, W., & Wang, Y. (2023). Time-sensitive data processing strategy for enhancing the performance of BFT consensus mechanism in IoT edge computing environment. In Proceedings of the 2023 2nd international conference on algorithms, data mining, and information technology (pp. 181-188). New York: Association for Computing Machinery. doi: 10.1145/3625403.3625436.
    20. Rizal, S., & Kim, D. (2025). Enhancing blockchain consensus mechanisms: A comprehensive survey on machine learning applications and optimisations. Blockchain: Research and Applications, 6(4), article number 100302. doi: 10.1016/j.bcra.2025.100302.
    21. Rodríguez-Camargo, C.D., Urquijo-Rodríguez, A.F., & Mojica-Nava, E.A. (2023). Consensus-based distributed optimisation for multi-agent systems over multiplex networks. ArXiv. doi: 10.48550/arXiv.2304.01875.
    22. Rupanetti, D., & Kaabouch, N. (2024). Combining edge computing-assisted internet of things security with artificial intelligence: Applications, challenges, and opportunities. Applied Sciences, 14(16), article number 7104. doi: 10.3390/app14167104.
    23. Salama, M., Hussein, Z., & El-Rahman, S.A. (2023). Evolution of blockchain consensus algorithms: A review on the latest milestones of blockchain consensus algorithms. Cybersecurity, 6(1), article number 30. doi: 10.1186/ s42400-023-00163-y.
    24. Sarker, K.U. (2024). Boolean Byzantine fault tolerant algorithm for light weight IoT consensus. Research Square. doi: 10.21203/rs.3.rs-4575514/v1.
    25. Tulkinbekov, K., & Kim, D. (2022). Blockchain-enabled approach for Big Data processing in edge computing. IEEE Internet of Things Journal, 9(19), 18473-18486. doi: 10.1109/JIOT.2022.3160838.
    26. Wang, Y., Wan, Q., Wu, Y., & Chen, L. (2025). Grouped Byzantine fault tolerant consensus algorithm based on aggregated signatures. Cybersecurity, 8(1), article number 60. doi: 10.1186/s42400-025-00362-9.
    27. Xiong, H., Chen, M., Wu, C., Zhao, Y., & Yi, W. (2022). Research on progress of blockchain consensus algorithm: A review on recent progress of blockchain consensus algorithms. Future Internet, 14(2), article number 47. doi: 10.3390/fi14020047.
    28. Xue, H., Chen, D., Zhang, N., Dai, H., & Yu, K. (2022). Integration of blockchain and edge computing in internet of things: A survey. Future Generation Computer Systems, 144, 307-326. doi: 10.1016/j.future.2022.10.029.
    29. Yuan, H., Li, F., Renhong, D., & Shu, T. (2024). Double‐layer Byzantine fault‐tolerant grouping consensus algorithm based on raft. IET Blockchain, 4(1), 555-569. doi: 10.1049/blc2.12073.
    30. Zhan, Z., & Huang, R. (2023). Improvement of hierarchical Byzantine fault tolerance algorithm in RAFT consensus algorithm election. Applied Sciences, 13(16), article number 9125. doi: 10.3390/app13169125.
    31. Zhang, C., Ji, L., Yang, S., Guo, X., & Li, H. (2024). Distributed optimal consensus control for multiagent systems based on event-triggered and prioritized experience replay strategies. Science China Information Sciences, 68(1), article number 112206. doi: 10.1007/s11432-023-4183-4.
    32. Zhong, W., Yang, C., Liang, W., Cai, J., Chen, L., Liao, J., & Xiong, N. (2023). Byzantine fault-tolerant consensus algorithms: A survey. Electronics, 12(18), article number 3801. doi: 10.3390/electronics12183801.