Optimising web interface performance using Amdahl’s law
Abstract
The article discussed the construction and application of generalised mathematical models based on Amdahl’s law to determine the maximum possible acceleration of web interfaces, taking into account their key features. An extension of the classical approach was proposed by including asynchronous processes, multi-level caching mechanisms, and dynamic resource loading methods in the model, which allows for a more accurate assessment of the cumulative impact of various optimisations on performance. In particular, the feasibility of taking into account asynchronous data exchange was justified, which allows processing requests in parallel and avoiding blockages in the process of updating content. A formula has been developed that takes into account the effectiveness of client and server caches and provides a quantitative assessment of the reduction in response time when reusing already loaded data. Particular attention was focused on step-by-step content retrieval techniques, where the initial page load was minimised by deferring the addition of individual scripts, images or styles, which speeds up the initial display of important content and makes the interface more responsive to user actions. In addition, the impact of a comprehensive combination of optimisation strategies on web interface performance was considered, and a corresponding generalised model was proposed, which uses an interdependence coefficient to determine the extent to which one optimisation enhances or, conversely, negates the effect of another. This makes it possible to predict the total performance gain and compare the cost of implementing several solutions with the potential time savings. The proposed formalised approach can serve as a basis for creating automated tools for evaluating web interface performance, integrated into the development process. Testing the model in three practical scenarios – partial rendering with API caching, JavaScript minification with a content delivery network (CDN), and code splitting with server-side caching – yielded performance gains of 1.87×, 1.55 ×, and 1.64 ×, respectively, which was fully consistent with theoretical predictions. The data obtained confirmed the ability of the R interdependence coefficient to accurately reflect the synergy or overlap of optimisation effects and makes the model suitable for pre-selecting the most effective acceleration strategies at the CI/CD audit stage
Keywords
mathematical models; asynchronous execution; data caching; dynamic loading; combined strategies; performance prediction
References
- Abounacer, R., Afdel, K., & Bouaouda, A. (2023). Resource utilization and cost implications of container live migration in clouds: An approach performed on Amazon Web Services (AWS). Research Square. doi: 10.21203/ rs.3.rs-3286731/v1.
- Ahmed, F., Erman, J., Ge, Z., Liu, A. X., Wang, J., & Yan, H. (2017). Detecting and localizing end-to-end performance degradation for cellular data services based on TCP loss ratio and round trip time. IEEE/ACM Transactions on Networking, 25(6), 3709-3722. doi: 10.1109/TNET.2017.2761758.
- Araújo, G.R., Gomes, R., Ferrão, P., & Gomes, M.G. (2024). Optimizing building retrofit through data analytics: A study of multi-objective optimization and surrogate models derived from energy performance certificates. Energy and Built Environment, 5(6), 889-899. doi: 10.1016/j.enbenv.2023.07.002.
- Dyvak, M., & Kindzerskyi, O. (2024). Investigation of the efficiency of parallel computational scheme for identification of interval discrete models based on swarm intelligence. Herald of Khmelnytskyi National University. Technical Sciences, 331(1), 29-37. doi: 10.31891/2307-5732-2024-331-3.
- Ekpobimi, H.O., Kandekere, R.C., & Fasanmade, A.A. (2024). Conceptual framework for enhancing front-end web performance: Strategies and best practices. Global Journal of Advanced Research and Reviews, 2(1), 99-107. doi: 10.58175/gjarr.2024.2.1.0032.
- Felani, R., Al-Azam, M.N., Adi, D.P., & Widodo, A. (2020). Optimizing virtual resources management using Docker on cloud applications. Indonesian Journal of Computing and Cybernetics Systems, 14(3), article number 319. doi: 10.22146/ijccs.57565.
- GeeksForGeeks. (2023). Computer organization. Amdahl’s law and its proof. Retrieved from https://www. geeksforgeeks.org/computer-organization-amdahls-law-and-its-proof.
- Hevery, M. (2025). Amdahl’s law. Bare metal JavaScript: The JavaScript virtual machine. Retrieved from https:// frontendmasters.com/courses/javascript-cpu-vm/amdahl-s-law.
- Jain, V. (2022). Optimizing web performance with lazy loading and code splitting. International Journal of Core Engineering & Management, 7(3), 193-199. doi: 10.5281/zenodo.14956631.
- John, J. (2024). Optimizing application performance: A study on the impact of caching strategies on latency reduction. Retrieved from https://www.researchgate.net/publication/385916660_OPTIMIZING_APPLICATION_ PERFORMANCE_A_STUDY_ON_THE_IMPACT_OF_CACHING_STRATEGIES_ON_LATENCY_REDUCTION.
- Junghans, C., Agarwal, A., & Delle Site, L. (2017). Computational efficiency and Amdahl’s law for the adaptive resolution simulation technique. Computer Physics Communications, 215, 20-25. doi: 10.1016/j.cpc.2017.01.030.
- Kothapalli, M. (2022). Performance analysis of single page applications. International Journal of Science and Research (IJSR), 11(1), 1631-1635. doi: 10.21275/SR24529184457.
- Kundos, M.H., Solovey, L.Ya., Hrysyuk, A.V., & Bahniuk, O.M. (2024). Efficiency and multi-threading of parallel calculations in systems programming. Таuridа Scientific Herald. Series: Technical Sciences, 5, 60-64. doi: 10.32782/ tnv-tech.2024.5.6.
- Kvurt, L., & Tsyhylyk, L. (2009). The use of Amdahl’s and Gustafson’s laws in evaluating the acceleration factor in multiprocessor systems. Measurement Techniques and Metrology, 70, 55-56.
- Ma, J. (2024). A high performance computing web search engine based on big data and parallel distributed models. Informatica, 48(20), 27-38. doi: 10.31449/inf.v48i20.6776.
- Majchrzak, T.A., Biørn-Hansen, A., & Grønli, T.-M. (2018). Progressive web apps: The definite approach to crossplatform development? In 51st Hawaii international conference on system sciences (HICSS 2018) (pp. 1-10). Hawaii: Hilton Waikoloa Village. doi: 10.24251/HICSS.2018.718.
- Malavolta, I., Chinnappan, K., Jasmontas, L., Gupta, S., & Soltany, K.A.K. (2020). Evaluating the impact of caching on the energy consumption and performance of progressive web apps. In Proceedings of the IEEE/ACM 7th international conference on mobile software engineering and systems (pp. 109-119). New York: Association for Computing Machinery. doi: 10.1145/3387905.3388593.
- Mitton, L. (2023). Amdahl’s law: Understanding the basics. Retrieved from https://www.splunk.com/en_us/blog/ learn/amdahls-law.html.
- Nair, A.M., Sivaiswarya, C.K., Sidharth, S., Visakh, K.K., & Joy, J. (2024). Dockerized application with web interface. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 10(2), 412-419. doi: 10.32628/CSEIT243646.
- Oh, S., Kwon, Y., & Lee, J. (2025). Optimizing real-time object detection in a multi-neural processing unit system. Sensors, 25(5), article number 1376. doi: 10.3390/s25051376.
- Poolla, C., & Saxena, R. (2022). On extending Amdahl’s law to learn computer performance. ArXiv, 2110, article number 07822. doi: 10.48550/arXiv.2110.07822.
- Potdar, A.M., Narayan, D.G., Kengond, S., & Mulla, M.M. (2020). Performance evaluation of Docker container and virtual machine. Procedia Computer Science, 171, 1419-1428. doi: 10.1016/j.procs.2020.04.152.
- Prus, O.V., Maidaniuk, V.P., & Arseniuk, I.R. (2024) March analysis of tools for multiproject environments management: Optimization of the software development. Scientific Works of VNTU, 1, 29-36. doi: 10.31649/2307-5376-2024-1-29-36.
- Ren, J., Gao, L., & Wang, Z. (2024). JavaScript performance tuning as a crowdsourced service. IEEE Transactions on Mobile Computing, 23(5), 6116-6132. doi: 10.1109/TMC.2023.3316167.
- Utomo, M.N.Y., Tungadi, E., & Khartika, W. (2025). Enhancing web performance for e-learning platform using content delivery network (CDN) and varnish cache. Journal of Information Systems and Informatics, 7(1), 831-847. doi: 10.51519/journalisi.v7i1.993.
- van Riet, J., Ghaleb, T.A. (2023). Optimise along the way: An industrial case study on web performance. Journal of Systems and Software, 198, article number 111593. doi: 10.1016/j.jss.2022.111593.
- Vepsäläinen, J., Hellas, A., & Vuorimaa, P. (2024). Overview of web application performance optimization techniques. ArXiv, 2412, article number 07892. doi: 10.48550/arXiv.2412.07892.