V.M. Vishnevsky, A.V. Gorbunova On the Application of Machine Learning Methods to Solving Problems Queuing Theory
V.M. Vishnevsky, A.V. Gorbunova On the Application of Machine Learning Methods to Solving Problems Queuing Theory

In this paper, for the first time, a systematic presentation of a new method for studying queuing systems (QS) using machine learning algorithms is presented. The publications considered in the review are divided into several categories - articles in which machine learning algorithms are used to predict the parameters of the QS of a technical nature and publications in which machine learning is used to estimate the probabilistic-temporal characteristics of the QS. The analysis of publications allows us to conclude that the application of machine learning methods is highly effective, the prospects for further research, as well as the possible separation of a new approach into an independent direction in the field of solving complex problems of queuing theory. 


queuing theory, queuing network, queuing system, fork-join queue, simulation, data mining, machine learning, artificial neural networks. 

PP. 70-82.

DOI 10.14357/20718632210407 

1. Vishnevsky V.M., Dudin A.N., Klimenok V.I. Stohasticheskie sistemy s korrelirovannymi potokami. Teoriya i primenenie v telekommunikacionnyh setyah [Stochastic systems with correlated flows. Theory and application in telecommunication networks] 1st ed. Moscow: Technosphera. 2018. 564 p.
2. Vishnevsky V.M. Teoreticheskie osnovy proektirovaniya komp'yuternyh setej [Theoretical foundations of computer network design] 1st ed. Moscow: Technosphera. 2003. 512 p.
3. Shvedov A.S. Approksimaciya funkcij s pomoshch'yu nejronnyh setej i nechetkih system [Functions approximating by neural networks and fuzzy systems] // Problemy upravleniya [Control Sciences]. 2018. No.1. P. 21-29.
4. Stone M.N. The generalized Weierstrass approximation theorem // Mathematics Magazine. 1948. 21(4): 167-183.
5. Khomonenko A.D., Yakovlev E.L. Nejrosetevaya approksimaciya harakteristik mnogokanal'nyh nemarkovskih sistem massovogo obsluzhivaniya [Neural network approximation of characteristics of multi-channel non-markovian queuing systems] // Trudy SPIIRAN [SPIIRAS Proceedings]. 2015. Vol.41. P. 81-93.
6. Mourõ R.N., Carvalho R.S., Carvalho R.N., Ramos G.N. Predicting Waiting Time Overflow on Bank Teller Queues // Proceedings of the 16th IEEE International Conference on Machine Learning and Applications (ICMLA). 2018. 842-847.
7. Khoshnevis B., Parisay S. Machine Learning and Simulation: Application in Queuing Systems // Simulation. 1993. Vol.61, No.5. P. 294-302.
8. Merlo G., Britos P., Rossi B., Garc'ia Mart'inez, R. Neural networks applied to automatic estimation of networks performance // Proceedings of the International Conference on Intelligent Systems and Control. 2004. P. 167-171.
9. Yousefi'zadeh H., Jonckheere H., Dynamic neural-based buffer management for queuing systems with self-similar characteristics // IEEE Transactions on Neural Networks. 2005. Vol.16. P. 1163-1173.
10. Li H., Gao H., Lv T., Lu Y. Deep q-learning based dynamic resource allocation for self-powered ultra-dense networks // IEEE international conference on communications workshops (ICC Workshops). 2018. P.1-6.
11. Sun H., Chen X., Shi Q., Hong M., Fu X., Sidiropoulos N.D. Learning to optimize: Training deep neural networks for wireless resource management // IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). 2017. P.1-6.
12. del-Hoyo-Alonso R., Fernández-de-Alarcón, P., Navamuel-Castillo J.-J., Medrano-Marqués N.J., Martin-del-Brio B., Fernández-Navajas J., Abadía-Gallego D. Neural Networks for QoS Network Management // Computational and Ambient Intelligence. 2007. Vol.4507. P.887-894.
13. Nie L., Jiang D., Yu S., Song H. Network traffic prediction based on deep belief network in wireless mesh back-bone networks // IEEE Wireless Communications and Networking Conference (WCNC). 2017. P.1-5.
14. Akbas A., Yildiz H., Ozbayoglu A., Tavli B. Neural network based instant parameter prediction for wireless sensor network optimization models // Wireless Network. 2019. Vol.25. P.3405-3418.
15. Ahad N., Qadir Ju., Ahsan N. Neural networks in wireless networks: Techniques, applications and guidelines // Journal of Network and Computer Applications. 2016. Vol.68. P.1-27.
16. Jiang C., Zhang H., Ren Y., Han Z., Chen K.-C., Hanzo L. Machine Learning Paradigms for Next-Generation Wireless Networks // IEEE Wireless Communications. 2017. Vol.24, No.2. P.98-105.
17. Mao Q., Hu F., Hao Q. Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey // IEEE Communications Surveys \& Tutorials. 2018. Vol.20, No.4. P. 2595-2621.
18. Luong N.C. et al. Applications of Deep Reinforcement Learning in Communications and Networking: A Survey // IEEE Communications Surveys \& Tutorials. 2019. Vol.21, No.4. P. 3133-31741.
19. Memon M.L., Maheshwari M.K., Saxena N., Roy A., Shin D.R. Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks // Electronics. 2019. Vol.8, No.7. Article Number: 778.
20. Ullah R., Marwat S.N.K., Ahmad A.M., Ahmed S., Hafeez A., Kamal T., Tufail M. A Machine Learning Approach for 5G SINR Prediction // Electronics. 2020. Vol.9, No.10. Article Number: 1660.
21. Kaur R., Kaur Sandhu J. and Sapra L. Machine Learning Technique for Wireless Sensor Networks // Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). 2020. P. 332-335.
22. Laha S., Chowdhury N., Karmakar R. How Can Machine Learning Impact on Wireless Network and IoT? – A Survey // 1th International Conference on Computing, Communication and Networking Technologies (ICCCNT). 2020. P. 1-7.
23. Bhatti M.A., Riaz R., Rizvi S.S., Shokat S., Riaz F., Kwon S.J. Outlier detection in indoor localization and Internet of Things (IoT) using machine learning // Journal of Communications and Networks. 2020. Vol.22, No.3. P. 236-243.
24. Morshedi M., Noll J. Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning // Sensors. 2021. Vol.21, No.2. Article Number: 621. P. 1-17.
25. Kyritsis A.I., Miche, D. A Machine Learning Approach to Waiting Time Prediction in Queueing Scenarios // Second IEEE International Conference on Artificial Intelligence for Industries. 2019. P.17-21
26. Hermanto R.P.S., Suharjito S., Nugroho, A. Waiting-Time Estimation in Bank Customer Queues using RPROP Neural Networks // Procedia Computer Science. 2018. Vol.135. P.35-42
27. Curtis C., Liu Ch., Bollerman Th.J., Pianykh O.S. Machine Learning for Predicting Patient Wait Times and Appointment Delays // Journal of the American College of Radiology. 2018. Vol.15, No.9. P.1310-1316.
28. Luís M.S. Dias, António A.C. Vieira, Guilherme A.B. Pereira, José A. Oliveira. Discrete Simulation Software Ranking – a Top list of the Worldwide most Popular and Used Tools // Proceedings of the 2016 Winter Simulation Conference (WSC). 2016. P. 1060-1071.
29. Bolch G., Greiner S., Meer H., Trivedi K.S. Queueing Networks and Markov Chains: Modeling and Performance Evaluation With Computer Science Applications. John Wiley&Sons, Inc., 2006.
30. Sivakami Sundaria M., Palaniammalb S. Simulation of M|M|1 Queuing System Using ANN // Malaya Journal of Matematik: Special Issue. 2015. No.1. P.279-294.
31. Sivakami Sundaria M., Palaniammalb, S. An ANN Simula-tion of Single Server with Infinite Capacity Queuing System // International Journal of Innovative Technology and Exploring Engineering. 2019. Vol.8 No.12. P. 4067-4071.
32. Sivakami Sundari M., Yamini S., Kalicharan Rath, Senthil Kumar S., Palaniammalb S. Artificial Neural Network simulation for Markovian Queuing Models in a Busy airport // International Conference on Computer Science, Engineering and Applications (ICCSEA). 2020. P. 1-6.
33. Gindin S.I., Khomonenko A.D., Adadurov S.E. CHislennyj raschet mnogokanal'noj sistemy massovogo obsluzhivaniya s rekurrentnym vhodyashchim potokom i ``razogrevom'' [Numerical calculations of multichannel queuing system with recurrent input and ``warm up''] // Izvestiya Peterburgskogo universiteta putey soobscheniya [Proceedings of Petersburg Transport University]. 2013. Vol.37, No.4. P. 92-101.
34. Gorbunova A.V., Vishnevsky V.M., Larionov A.A. Evaluation of the End-to-End Delay of a Multiphase Queuing System Using Artificial Neural Networks // Lecture Notes in Computer Science. 2020. Vol.12563. P. 631-642.
35. Rabta B. A review of decomposition methods for open queueing networks // Reiner G. (eds.) Rapid Modelling for Increasing Competitiveness. Springer, London. 2009. P. 25-42.
36. Gorbunova A.V., Vishnevsky V.M. Estimating the Response Time of a Cloud Computing System with the Help of Neural Networks // Advances in Systems Science and Applications. 2020. Vol.20. No.3. P. 105-112.
37. Gorbunova A.V., Lebedev A.V. Response Time Estimate for a Fork-join System with Pareto Distributed Service Time as a Model of a Cloud Computing System Using Neural Networks // Communications in Computer and Information Science. 2021. In print.
38. Gorbunova A.V., Vishnevsky V.M. Evaluation of the Performance Parameters of a Closed Queuing Network Using Artificial Neural Networks // Lecture Notes in Computer Science. 2021. In print
39. Vishnevsky V.M., Semenova O.V., Bui D.T. Using a machine learning for analysis of polling systems with corre-lated arrivals // Lecture Notes in Computer Science. 2021. In print
40. Efrosinin D, Stepanova N. Estimation of the Optimal Threshold Policy in a Queue with Heterogeneous Servers Using a Heuristic Solution and Artificial Neural Networks. Mathematics. 2021. Vol.9, No.11. Article Number: 1267
41. Lazareva V.E., Larionov A.A., Muhtarov A.A. Raschyot mezhkoncevyh zaderzhek i dlin ocheredej v mnogoshagovoj tandemnoj seti s primeneniem metodov mashinnogo obucheniya [The calculation of end-to-end delays and queue sizes in a tandem network using machine learning methods] // Proceedings of the XI Conference (with international participation) "Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems" (ITTMM). 2020. P. 43-48.
42. Vishnevskij V.M., Larionov A.A., Muhtarov A.A. Raschyot harakteristik tandemnoj seti s fiksirovannymi dlinami vhodyashchih paketov metodom mashinnogo obucheniya [Calculating the characteristics of a tandem network with fixed lengths of incoming packets using machine learning] // Proceedings of the XIII International Conference "Computer-Aided Technologies in Applied Mathematics" (ICAM). 2020. P. 82-84.
43. Dudin A.N., Klimenok V.I., Vishnevsky V.M. The theory of queuing systems with correlated flows. 1st ed. Heidel-berg: Springer. 2020. 410 p.
2024 / 01
2023 / 04
2023 / 03
2023 / 02

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".