https://doi.org/10.62660/bcstu/4.2025.143

Volume 30, No. 4, 2025

143-154

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    • Reactive tracing of behavioural scenarios in single-page applications by integrating Bun-based WebSocket channels and OpenTelemetry

    Vladyslav Ananchenko Yuriy Lotyuk

    Received 18.06.2025, Revised 11.11.2025, Accepted 15.12.2025

    Abstract

    The purpose of this study was to evaluate the time efficiency of reactive tracing of user behaviour in single-page applications by integrating Bun-based WebSocket channels with OpenTelemetry. The methodology included creating a prototype application in React, high-frequency monitoring and aggregation of SCADA data, building and optimising a 64-32-16 neural network in TensorFlow, simulations in MATLAB/Simscape, and statistical analysis using Theil-Sen regression, Seasonal and Trend decomposition, Brown-Forsyth test, two-factor analysis of variance, bootstrap permutation, Dickey-Fuller test, and Kaplan-Meier survival curves. The findings revealed that the combination of Hypertext Transfer Protocol with binary serialisation in Protocol Buffers format provided the lowest event detection latency, which averaged 45.09 milliseconds, and the lowest transmission latency, which reached only 62.83 milliseconds in the form-filling scenario. At the same time, the combination of websockets with JavaScript Object Notation text format demonstrated the highest latency, with an average event detection rate of 69.99 milliseconds and transmission latency of up to 88.1 milliseconds, as well as the highest variability in response time. Statistical analysis confirmed the substantial differences between all configurations: the results of the analysis of variance revealed extremely high F-statistics for both indicators with a p-value of less than 0.000001, indicating that both the protocol and the serialisation format have a real impact on the time efficiency. Additionally, the study found that the event detection delay and the transmission delay were independent variables, as the correlation coefficients stayed close to zero in all cases. Thus, the most suitable configuration for high-frequency telemetry systems was a hypertext protocol with a binary Protocol Buffers format, which ensures not only minimal time delays but also stability in loaded environments. The practical significance of the findings lies in the possibility of using them by performance engineers, front-end architects, and developers of monitoring systems to create efficient and scalable solutions focused on analysing user behaviour in real time

    Keywords:

    time delays; asynchrony; binary serialisation; event processing; HTTP-JSON; telemetry architecture

    Suggested citation
    Ananchenko, V., & Lotyuk, Yu. (2025). Reactive tracing of behavioural scenarios in single-page applications by integrating Bun-based WebSocket channels and OpenTelemetry. Bulletin of Cherkasy State Technological University, 30(4), 143-154. https://doi.org/10.62660/bcstu/4.2025.143
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    References

    1. Amirkhanov, B., Amirkhanova, G., Kunelbayev, M., Adilzhanova, S., & Tokhtassyn, M. (2025). Evaluating HTTP, MQTT over TCP and MQTT over WEBSOCKET for digital twin applications: A comparative analysis on latency, stability, and integration. International Journal of Innovative Research and Scientific Studies, 8(1), 679-694. doi: 10.53894/ijirss.v8i1.4414.
    2. Deeter, T., Green, D.H., Kidwell, S., Kane, T.J., Donnal, J.S., Vasquez, K., Sievenpiper, B., & Leeb, S.B. (2021). Behavioral modeling for microgrid simulation. IEEE Access, 9, 35633-35645. doi: 10.1109/access.2021.3061891.
    3. Donta, P.K., Srirama, S.N., Amgoth, T., & Annavarapu, C.S.R. (2021). Survey on recent advances in IoT application layer protocols and machine learning scope for research directions. Digital Communications and Networks, 8(5), 727-744. doi: 10.1016/j.dcan.2021.10.004.
    4. Eder, C., Winzinger, S., & Lichtenthäler, R. (2023). A comparison of distributed tracing tools in serverless applications. In 2023 IEEE international conference on service-oriented system engineering (SOSE) (pp. 98-105). Athens: Institute of Electrical and Electronics Engineers. doi: 10.1109/SOSE58276.2023.00018.
    5. Faizulin, O., & Nazarkevych, M. (2024). Methods and means of analyzing application security via distributed tracing. Journal of Lviv Polytechnic National University “Information Systems and Networks”, 16, 69-87. doi: 10.23939/sisn2024.16.069.
    6. Fernández, F., Zverev, M., Garrido, P., Juárez, J.R., Bilbao, J., & Agüero, R. (2021). Even lower latency in IIoT: Evaluation of QUIC in industrial IoT scenarios. Sensors, 21(17), article number 5737. doi: 10.3390/ s21175737.
    7. Huang, C., et al. (2022). Artificial intelligence enabled radio propagation for communications – part II: Scenario identification and channel modeling. IEEE Transactions on Antennas and Propagation, 70(6), 3955-3969. doi: 10.1109/tap.2022.3149665.
    8. Huang, W., Zhang, L., Wu, H., Min, F., & Song, A. (2022). Channel-Equalization-HAR: A light-weight convolutional neural network for wearable sensor based human activity recognition. IEEE Transactions on Mobile Computing, 22(9), 5064-5077. doi: 10.1109/tmc.2022.3174816.
    9. Hunko, I. (2025). Adaptive approaches to software testing with embedded artificial intelligence in dynamic environments. International Journal of Current Science Research and Review, 8(5), 2036-2051. doi: 10.47191/ ijcsrr/v8-i5-10.
    10. Iori, F., Perovic, G., Cini, F., Mazzeo, A., Falotico, E., & Controzzi, M. (2023). DMP-based reactive robot-to-human handover in perturbed scenarios. International Journal of Social Robotics, 15(2), 233-248. doi: 10.1007/s12369022-00960-4.
    11. Jackson, S., Cummings, N., & Khan, S. (2024). Streaming technologies and serialization protocols: Empirical performance analysis. ArXiv. doi: 10.48550/arXiv.2407.13494.
    12. Kaliiukh, Iu., et al. (2025). Application of Digital Twins and IoT for investigating damage caused to buildings under dynamic influences. In Proceedings of the fib symposium in Antibes (pp. 3069-3073). Antibes: fib. The International Federation for Structural Concrete.
    13. Keerativoranan, N., Saito, K., & Takada, J. (2024). Grid-based channel modeling technique for scenario-specific wireless channel emulator based on path parameters interpolation. IEEE Open Journal of the Communications Society, 5, 1724-1739. doi: 10.1109/ojcoms.2024.3373538.
    14. Kolodii, R. (2024). Unpacking Russia’s cyber-incident response. Security Studies, 33(4), 640-669. doi: 10.1080/09636412.2024.2391757.
    15. Li, Q., Peng, Z., Feng, L., Liu, Z., Duan, C., Mo, W., & Zhou, B. (2023). ScenarioNet: Open-source platform for largescale traffic scenario simulation and modeling. In NIPS ‘23: Proceedings of the 37th international conference on neural information processing systems (pp. 3894-3920). New Orleans: Neural Information Processing Systems Foundation, Inc.
    16. Li, R., Sun, J., Xue, J., & Masouros, C. (2023). Scenario-aware learning approaches to adaptive channel estimation. IEEE Transactions on Communications, 72(2), 874-889. doi: 10.1109/tcomm.2023.3330878.
    17. Lin, C., He, J., Shen, C., Li, Q., & Wang, Q. (2022). CrossBehaAuth: Cross-scenario behavioral biometrics authentication using keystroke dynamics. IEEE Transactions on Dependable and Secure Computing, 20(3), 2314-2327. doi: 10.1109/tdsc.2022.3179603.
    18. Maltsev, E., & Amin, R.U. (2024). Impact of serialization format on inter-service latency. Advances in CyberPhysical Systems, 9(2), 89-94. doi: 10.23939/acps2024.02.089.
    19. Mbugua, A.W., Chen, Y., Raschkowski, L., Thiele, L., Jaeckel, S., & Fan, W. (2020). Review on ray tracing channel simulation accuracy in sub-6 GHz outdoor deployment scenarios. IEEE Open Journal of Antennas and Propagation, 2, 22-37. doi: 10.1109/ojap.2020.3041953.
    20. Mengjiao, Z., Yu, L., Jie, H., Ruisi, H., Jingfan, Z., Chongyang, Y., & Chengxiang, W. (2024). Artificial intelligence based multi-scenario mmWave channel modeling for intelligent high-speed train communications. China Communications, 21(3), 260-272. doi: 10.23919/jcc.ja.2022-0406.
    21. Nõu, A., Talluri, S., Iosup, A., & Bonetta, D. (2025). Investigating performance overhead of distributed tracing in microservices and serverless systems. In ICPE ‘25: Companion of the 16th ACM/SPEC international conference on performance engineering (pp. 162-166). New York: Association for Computing Machinery. doi: 10.1145/3680256.3721316.
    22. Perin, G., Wu, L., & Picek, S. (2022). Exploring feature selection scenarios for deep learning-based side-channel analysis. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2022(4), 828-861. doi: 10.46586/ tches.v2022.i4.828-861.
    23. Salomon, D., Weiss, A., & Levi, I. (2021). Improved filtering techniques for single- and multi-trace side-channel analysis. Cryptography, 5(3), article number 24. doi: 10.3390/cryptography5030024.
    24. Sanchez, P.M.S., Valero, J.M.J., Celdran, A.H., Bovet, G., Perez, M.G., & Perez, G.M. (2021). A survey on device behavior fingerprinting: Data sources, techniques, application scenarios, and datasets. IEEE Communications Surveys & Tutorials, 23(2), 1048-1077. doi: 10.1109/comst.2021.3064259.
    25. Shuangde, L., Yuanjian, L., Leke, L., Xijia, B., Guyue, Z., Yaxin, Y., Anwen, R., & Qi, S. (2021). Millimeter wave channel characteristics of outdoor microcellular based on improved ray tracing method and BP neural network algorithm. Chinese Journal of Radio Science, 36(3), 430-442. doi: 10.12265/j.cjors.2020217.
    26. Steinmann, P., Auping, W.L., & Kwakkel, J.H. (2020). Behavior-based scenario discovery using time series clustering. Technological Forecasting and Social Change, 156, article number 120052. doi: 10.1016/j.techfore.2020.120052.
    27. Thakur, A., & Chandak, M.B. (2022). A review on opentelemetry and HTTP implementation. International Journal of Health Sciences, 6(S2), 15013-15023. doi: 10.53730/ijhs.v6ns2.8972.
    28. Tian, Y., Li, H., Zhu, Q., Mao, K., Ali, F., Chen, X., & Zhong, W. (2024). Generative network-based channel modeling and generation for air-to-ground communication scenarios. IEEE Communications Letters, 28(4), 892-896. doi: 10.1109/lcomm.2024.3363621.
    29. Wang, C., Lv, Z., Gao, X., You, X., Hao, Y., & Haas, H. (2022). Pervasive wireless channel modeling theory and applications to 6G GBSMs for all frequency bands and all scenarios. IEEE Transactions on Vehicular Technology, 71(9), 9159-9173. doi: 10.1109/tvt.2022.3179695.
    30. Xiong, L., Yao, Z., Miao, H., & Ai, B. (2021). Vehicle-to-vehicle channel characterization based on ray-tracing for urban road scenarios. Wireless Communications and Mobile Computing. doi: 10.1155/2021/8854247.
    31. Yang, M., et al. (2020). Machine-learning-based scenario identification using channel characteristics in intelligent vehicular communications. IEEE Transactions on Intelligent Transportation Systems, 22(7), 39613974. doi: 10.1109/tits.2020.3001132.
    32. Zhang, J., & Wu, X. (2024). An anti-jamming game between dynamically-sensing jammer and legitimate user with faking-slot transmission. IEEE Transactions on Vehicular Technology, 73(7), 10287-10300. doi: 10.1109/ tvt.2024.3372969.
    33. Zhang, J., Lin, J., Tang, P., Fan, W., Yuan, Z., Liu, X., Xu, H., Lyu, Y., Tian, L., & Zhang, P. (2024). Deterministic ray tracing: A promising approach to THZ channel modeling in 6G deployment scenarios. IEEE Communications Magazine, 62(2), 48-54. doi: 10.1109/mcom.001.2200486.
    34. Zhang, J., Liu, L., Fan, Y., Zhuang, L., Zhou, T., & Piao, Z. (2020). Wireless channel propagation scenarios identification: A perspective of machine learning. IEEE Access, 8, 47797-47806. doi: 10.1109/ access.2020.2979220.
    35. Zheng, Z., Trivedi, K.S., Wang, N., & Qiu, K. (2017). Markov regenerative models of webservers for their user-perceived availability and bottlenecks. IEEE Transactions on Dependable and Secure Computing, 17(1), 92-105. doi: 10.1109/tdsc.2017.2753803.
    36. Zhou, W., Borjigin, A., & He, C. (2025). Behavioral Universe Network (BUN): A behavioral information-based framework for complex systems. ArXiv. doi: 10.48550/arXiv.2504.15146.