The popularity of web services has increased the demand for quality indicators of remote service systems. One of the indicators of the quality of such a service system is the response time of the system users. This time depends on several indicators, and if it exceeds a certain value, it causes inconvenience to the users of the system. Works based on previously conducted experimental investigations have a limited use. Unlike experimental studies, this research proposes using public service theory models to estimate the response time of user requests. Studies were done to identify system quality indicators using the suggested mathematical model. Based on the model, the waiting time for request responses, the dependence of the waiting time on the number of users, and the dependence of the waiting time on the service system's internal technical indicators were investigated. The studies done showed that the proposed model is completely consistent with the results obtained by the present experimental approach and may be widely employed in research.
The popularity of web services has increased the demand for quality indicators of remote service systems. One of the indicators of the quality of such a service system is the response time of the system users. This time depends on several indicators, and if it exceeds a certain value, it causes inconvenience to the users of the system. Works based on previously conducted experimental investigations have a limited use. Unlike experimental studies, this research proposes using public service theory models to estimate the response time of user requests. Studies were done to identify system quality indicators using the suggested mathematical model. Based on the model, the waiting time for request responses, the dependence of the waiting time on the number of users, and the dependence of the waiting time on the service system's internal technical indicators were investigated. The studies done showed that the proposed model is completely consistent with the results obtained by the present experimental approach and may be widely employed in research.
| № | Муаллифнинг исми | Лавозими | Ташкилот номи |
|---|---|---|---|
| 1 | Zakirov V.. | proofessor v.b. | Toshkent davlat transport universiteti |
| 2 | Abdullayev E.. | doktorant | Toshkent davlat transport universiteti |
| № | Ҳавола номи |
|---|---|
| 1 | Manchanda P. Analysis of optimisation techniques to improve user response time of web applications and their implementation for MOODLE // Advances in Information Technology: 6th International Conference, IAIT 2013, Bangkok, Thailand, December 12–13, 2013. Proceedings 6. Springer International Publishing, pp. 150-161, 2013 |
| 2 | Özüdoğru G. Problems faced in distance education during the COVID-19 pandemic // Participatory Educational Research, Vol. 8, pp. 321-333, 2021. |
| 3 | Abdullaev E., Zakirov V., Shukurov F. Assessment of the distance learning server's operation strategies and service capacity in advance //E3S Web of Conferences. – EDP Sciences, Vol. 420, pp. 06016, 2023. |
| 4 | Barral H., Jaloyan G.A., Thomas-Brans F., Regnery M., Géraud-Stewart R., Heckmann T., Souvignet T., Naccache D. A forensic analysis of Google Home: Repairing compressed data without error correction //Forensic Science International: Digital Investigation, Vol. 42, pp. 301437, 2022. |
| 5 | Schwarte A., Haase P., Hose K., Schenkel R., Schmidt M. Fedx: Optimisation techniques for federated query processing on linked data //The Semantic Web-ISWC 2011: 10th International Semantic Web Conference, Bonn, Germany, October 23–27, 2011, Proceedings, Part I 10, pp. 601-616, Springer Berlin Heidelberg 2011. |
| 6 | Lee M., Lee M., and Kim C.S., A JIT CompilationBased Unified SQL Query Optimisation System, 6th International Conference on IT Convergence and Security (ICITCS), pp. 1-2, IEEE 2016. |
| 7 | Vakhid Z., Eldor A., Farrukh S. System's load reduction by using asynchronous and synchronous service methods //Universum: technical science, Vol. 4-6 (109), pp. 65-70, 2023. |
| 8 | Zakirov V., Abdullaev E., Determining the efficiency of service quality in the open loss and waiting methods of single-channel synchronous systems // Current issues in the development of innovative information technologies in transport, Vol. 1, No. 2, pp. 22–33, 2022. |
| 9 | Vora M. N., Shah D., Estimating effective web server response time, 2017 Second International Conference on Information Systems Engineering (ICISE), pp. 37–44, IEEE 2017. |
| 10 | Kurbanov F., Yaronova N.V., Kodirova L.A., "Remote Control and Monitoring of the Unguarded Railway Crossing System," 2023 International Russian Automation Conference (RusAutoCon), Sochi, Russian Federation, pp.993–997, 2023. doi: 10.1109/RusAutoCon58002.2023.10272764. |
| 11 | Khazaei, H., Misic, J., Misic, V.B. Performance analysis of cloud computing centres using m/g/m+r queuing systems IEEE Transactions on Parallel and Distributed Systems, Vol. 23(5), 936–943, 2011. |
| 12 | Youcef, S., Bhatti, M. U., Mokdad, L., Monfort, V. Simulation-based response-time analysis of composite Web services. 2006 IEEE International Multitopic Conference, pp. 349–354, IEEE 2006. |
| 13 | Almeida, L., Pedreiras, P. Scheduling within temporal partitions: response-time analysis and server design. In Proceedings of the 4th ACM International Conference on Embedded Software, pp. 95–103, 2004. |
| 14 | Chiew T.K., Renaud K. Estimating web page response time based on server access log //2015 9th Malaysian Software Engineering Conference (MySEC), pp. 140–144, IEEE 2015. |
| 15 | Lozhkovsky A.G. Theory of Queuing in Telecommunications: A Textbook //Odessa: ONAS im. AS Popova, 2012. |
| 16 | Sharma D. Response time-based balancing of load in web server clusters. 7th International Conference on Reliability, Infocom Technologies, and Optimisation (Trends and Future Directions) (ICRITO), pp. 471-476, 2018 |
| 17 | Zhang X., Zhang J., Peng C., Wang X. Multimodal optimisation of edge server placement considering system response time //ACM Transactions on Sensor Networks, Vol. 19, pp. 1–20, 2022. |
| 18 | Tong Z., Deng X., Mei J., Liu B., Li K. Response time and energy consumption co-offloading with the SLRTA algorithm in cloud-edge collaborative computing // Future Generation Computer Systems, Vol. 129, pp. 64–76, 2022. |
| 19 | Huang C., Huang G., Liu W., Wang R., Xie M. A parallel joint optimised relay selection protocol for wake-up radio-enabled WSNs //Physical Communication, Vol. 47, pp. 101320, 2021. |
| 20 | Bocchi E., De Cicco L., Rossi D. Measuring the quality of experience of web users //ACM SIGCOMM Computer Communication Review, Vol. 46, pp. 8–13, 2016. |
| 21 | Zhong H., Fang Y., Cui J. Reprint of “LBBSRT: An efficient SDN load balancing scheme based on server response time” Future Generation Computer Systems, Vol. 80, pp. 409–416, 2018. |
| 22 | Cao K., Li L., Cui Y., Wei T., Hu S. Exploring placement of heterogeneous edge servers for response time minimization in mobile edge-cloud computing //IEEE Transactions on Industrial Informatics, Vol. 17, pp. 494-503, 2020 |
| 23 | Tochukwu N.J., Mary O.E.C. Performance Evaluation of Web Servers Using Response Time and Bandwidth //Performance Evaluation, Vol. 9. pp. 133–138, 2020. |
| 24 | Martinez J., Dasari D., Hamann A., Sañudo I., Bertogna M. Exact response time analysis of fixed priority systems based on sporadic servers, Journal of Systems Architecture, Vol. 110, pp. 101836, 2020. |
| 25 | Ergüzen, A., Erdal, E., Ünver, M., Özcan. A. Improving the technological infrastructure of distance education through trustworthy platform-independent virtual software application pools //Applied Sciences, Vol. 11. – no. 3. – pp. 1214, 2021. |