Abstract
The paper investigates stochastic polling systems using machine learning. M/M/1 and MAP/M/1-type polling systems with cyclic polling, as well as M/M/1-type polling systems with adaptive cyclic polling are considered. To train a machine model of a M/M/1-type polling system, we used the results of analytical calculations, and for other considered systems that do not allow exact analysis, we used the simulation results. Numerical examples are given, and it is shown that the results of machine learning are close enough to the results of analytical or simulation calculations.
The research is supported by the Russian Foundation for Basic Research, project no. 19-29-06043.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Vishnevsky, V., Semenova, O.: Polling systems and their application to telecommunication networks. Mathematics 9(2), 117 (2021)
Boon, M.A.A., van der Mei, R.D., Winands, E.M.M.: Applications of polling systems. Surv. Oper. Res. Manage. Sci. 16(2), 67–82 (2011)
Vishnevsky, V.M., Semenova, O.V., Bui, D.T.: Software complex for evaluating the characteristics of stochastic polling systems: Certificate of state registration of a computer program No. 2019614554 of the Russian Federation; Registered 04 April 2019
Cybenko, J.: Approximations by superpositions of a sigmoidal function. Math. Control Signals Syst. 2(4), 303–314 (1989)
Sivakami Sundari, M., Palaniammal, S.: Simulation of \(M/M/1\) queuing system using ANN. Malaya J. Matematik 1, 279–294 (2015)
Sivakami Sundari, M., Palaniammal, S.: An ANN simulation of single server with infinite capacity queuing system. Int. J. Innovative Technol. Exploring Eng. 8(12), 4067–4071 (2019)
Csáji, B.C.: Approximation with artificial neural networks. Eötvös Loránd University, Hungary, Faculty of Sciences (2001)
Thomas, A., Petridis, M., Walters, S., Malekshahi, S., Morgan, R.: Two hidden layers are usually better than one. In: International Conference on Engineering Applications of Neural Networks (2017). https://doi.org/10.1007/978-3-319-65172-9_24
Heaton, J.: Introduction to Neural Networks with Java, Heaton Research, Inc., Chesterfield (2008)
Yechiali, U.: Analysis and control of polling systems. In: Donatiello, L., Nelson, R. (eds.) Performance/SIGMETRICS -1993. LNCS, vol. 729, pp. 630–650. Springer, Heidelberg (1993). https://doi.org/10.1007/BFb0013871
Dudin, A.N., Klimenok, V.I., Vishnevsky, V.M.: The Theory of Queuing Systems with Correlated Flows. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-32072-0
Vishnevsky, V.M., Dudin, A.N., et. al.: Approximate method to study \(M/G/1\)-type polling system with adaptive polling mechanism. Qual. Technol. Quant. Manage. 9(2), 211–228 (2012)
Vishnevsky, V.M., Semenova, O.V., Bui, D.T., Sokolov, A.: Adaptive cyclic polling systems: analysis and application to the broadband wireless networks. In: Vishnevskiy, V.M., Samouylov, K.E., Kozyrev, D.V. (eds.) DCCN 2019. LNCS, vol. 11965, pp. 30–42. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36614-8_3
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Vishnevsky, V., Semenova, O., Bui, D.T. (2021). Using a Machine Learning Approach for Analysis of Polling Systems with Correlated Arrivals. In: Vishnevskiy, V.M., Samouylov, K.E., Kozyrev, D.V. (eds) Distributed Computer and Communication Networks: Control, Computation, Communications. DCCN 2021. Lecture Notes in Computer Science(), vol 13144. Springer, Cham. https://doi.org/10.1007/978-3-030-92507-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-92507-9_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92506-2
Online ISBN: 978-3-030-92507-9
eBook Packages: Computer ScienceComputer Science (R0)