Modeling of Decision-making Processes to Ensure Sustainable Operation of Multiservice Communication Network

Alevtina Aleksandrovna Muradova

doi:10.5614/itbj.ict.res.appl.2019.13.1.4

Authors

Alevtina Aleksandrovna Muradova Department of Telecommunication Engineering, Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, A. Temur St., 108, Tashkent, 100084

DOI:

https://doi.org/10.5614/itbj.ict.res.appl.2019.13.1.4

Keywords:

algorithm of rational choice of alternatives, energy factors, falsified information, fuzzy set theory, maintenance factors, multi-service communications network, structurally complex system, method of non-dominated alternatives, system fuzzy-logic models

Abstract

This paper shows the modeling of decision-making processes to ensure stable operation of multiservice communication networks (MCNs) using the mathematical apparatus of fuzzy logic models. A classification of the main factors affecting the stability of an MCN is given. The main factors affecting the structural stability of MCNs are external factors, internal factors, energy factors, and maintenance factors. A decision-making strategy (DM) was chosen. The main factors that affect the stability of the functioning of an MCN are characterized by heterogeneity. Therefore, the task of the DM to ensure stability of the functioning of the MCN was reduced to producing a sequential solution of the following interrelated tasks: identification of the MCN by a systematic analysis of the main factors affecting the stability of the MCN, ranking the states of the MCN, and definition of the decision-making criteria. The first point is implemented by setting up a complex model of the MCN based on integration of the principles of fuzzy set theory (FST). A promising method for choosing a rational alternative is the method of non-dominated alternatives (MNDA), based on the aggregation of fuzzy information to characterize the relationship between the alternatives according to certain criteria.

Downloads

Download data is not yet available.

References

Brunsch, D., Max-Plus Algebraic Modeling and Control of High-Throughput Screening Systems, in preprint of the 2nd IFAC Workshop on Dependable Control of Discrete Systems, Bari, Italy, pp. 103-108, 2009.

Baccelli, F., Synchronization and Linearity an Algebra for Discrete Event Systems, Jean-Pierre Quadrat, Wiley, 2001.

Kotsynyak, M.A., Kuleshov, I.A. & Lauta, O.S., Information and Telecommunication Networks Resiliency, Saint Petersburg, Polytechnic University Publ., p.92, 2013 (In Russian).

ITU-T, General Overview of NGN, ITU-T Recommendation Y. 2001, 12/2004.

Park, K.I., QoS in Packet Networks, New York, United States, Springer, 2004.

Zheng, Y., Dou, W., Tian, J. & Xiao, M., An Overview of Research on QoS Routing in Advanced Parallel Processing Technologies, Lecture Notes in Computer Science, 2834, Springer, pp. 387-397, 2003.

Adams, R., Active Queue Management: A Survey, IEEE Communications Surveys & Tutorials, 99, pp. 1-52, 2012.

Lemeshko, O. & Yevsyeyeva, O., Tensor Geometrization of Structural and Functional Representation of Telecommunication System in the Basis of Interpoles paths and Internal Cuts, Scientific notes of UNDIZ, 1(13), pp. 14-26, 2010. (in Russian)

Lemeshko, O., Tensor Model of Multipath Routing of Aggregated Flows with Reservation Network Resources, that is Provided in a Curvature Space, Transactions of UNDIRT, 4(40), pp. 12-18, 2004. (in Russian)

Network Performance Objectives for IP-based Services, ITU-T Recommendation Y. 1541, 05/2002.

Yevsyeyeva, O., Classification of Spaces and Bases Used in the Geometric Description of the Structure of Telecommunication Network, Radiotekhnika, 159, pp. 14-19, 2009. (in Russian)

Bugaichenko, D., Design and Implementation of Methods Considering Formal and Logical Specifications of Self-tuning Multiagent Systems with Time Constraints, PhD thesis, SPb., p. 259, 2007.

Gerasina, A.V., Adaptive Fuzzy Prediction of Traffic in Information and Telecommunication Networks, Information Processing Systems, 9(116), pp. 141-145, 2013.

Gowrishankar, S. & Satyanarayana, P.S., A Time Series Modeling and Prediction of Wireless Network Traffic, Intern. Journal of Interactive Mobile Technologies (IJIM), 3(1), pp. 53-62, 2009.

Levin, V.I., Evaluation of Reliability of Systems by Logical Methods. Systems of Control, Communication and Security, n. 2, pp. 182-195, 2017 (in Russian).

Levin, V. I., Logical Methods in Theory of Reliability. II. Mathematical model of Reliability, Transaction of the TSTU, 16(1), pp. 119-132, 2010. (in Russian)

Song, Q., A Note on Fuzzy Time Series Model Selection with Sample Autocorrelation Functions, Cybernetics and Systems: An International Journal, 34, pp. 93-107, 2003.

Tsaur, R.C., Yang, J. C. & Wang, H. F., Fuzzy Relation Analysis in Fuzzy Time Series Model, Computers and Mathematics with Applications, 49, pp. 539-548, 2005.

Vegesna, S., IP Quality of Service, Srinivas Vegesna, Cisco Press. p. 368, 2001.

Mika, P., Georg, M., Hisham, K. & Aki, N., IMS. IP Multimedia Concepts and Services in the Mobile Domain, John Wiley & Sons Ltd. p. 419, 2004.