Synthesis of the topological structure of a self-healing and scalable data network segment

DOI: 10.31673/2412-4338.2020.049516

Abstract

An approach is proposed to reduce the load on the backbone channels of the data network. The scientific novelty lies in the improvement of the method for reducing the load of backbone channels by synthesizing the topological structure of its segments, which have the properties of local self-healing and scalability, and segment recovery and scaling occur without access to the backbone channels of the main network. The following tasks were solved: the structure of the primary regular communication network was formed, assuming the possibility of self-recovery; an algorithm for the formation of a secondary communication network of a local segment based on the existing primary regular network has been developed; the principles of local scaling are formulated; a method for local scaling of the topological structure of data transmission network segments is proposed; the comparative analysis of the load of the main channels of the data transmission network was carried out using the standard and the proposed variants of the synthesis of its local components. A regular graph with a given connectivity is used as the initial structure. Further transformations were carried out using a modified combinatorial optimization method. The topological features of the "grid" type graph are used to scale the segment. With a large number of segment switching nodes, the additional load on the trunk channels increases with the proposed approach more slowly than with the standard one. The main computational difficulties arise when applying the method of indefinite Lagrange multipliers. Therefore, the development of this study may consist in reducing the computational complexity of the algorithm for forming a secondary communication network of the segment under consideration.

Keywords: primary and secondary communication networks, self-healing, local segment, backbone channel, scalability.

References
1. Shefer, О.V. and Alnaeri, Frhat Ali (2020), “Optimum flow distribution in the network with adaptive data transfer”, Electronics and Control Systems, No. 4(66), pp.45-50.
2. Fang, Shuguang; Dong, Yuning and Shi, Haixian (2012), “Approximate Modeling of Wireless Channel Based on Service Process Burstiness”, Proceedings of the International Conference on Wireless Networks (ICWN); Athens: 1-7. Athens: The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing, (WorldComp).
3. Sobieraj M., Stasiak, M and Weissenberg, J. (2012), “Analytical model of the single threshold mechanism with hysteresis for multi-service networks”, IEICE Transactions on Communications, Vol. E95.B No. 1, pp. 120–132.
4. Kuchuk G., Kovalenko A., Komari I.E. and Svyrydov A., Kharchenko V. (2019), “Improving Big Data Centers Energy Efficiency: Traffic Based Model and Method”, In: Kharchenko V., Kondratenko Y., Kacprzyk J. (eds) Green IT Engineering: Social, Business and Industrial Applications. Studies in Systems, Decision and Control, vol 171. Springer, Cham.
5. Mashkov O.A., Sobchuk V.V., Barabash O.V., Dakhno N.B., Shevchenko H.V. and Maisak T.V. (2019), “Improvement of variational-gradient method in dynamical systems of automated control for integro-differential models”. Mathematical Modeling and Computing. Vol. 6. No. 2. P. 344 – 357.
6. Barabash O.V., Dakhno N.B., Shevchenko H.V. and Majsak T.V. (2017), “Dynamic Models of Decision Support Systems for Controlling UAV by Two-Step Variational-Gradient Method”. Proceedings of 2017 IEEE 4th International Conference “Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD)”, October 17-19, 2017, Kyiv, Ukraine. National Aviation University. P. 108 – 111.
7. Kurose, J. and Ross, K. (2017), Computer networking: a top–down approach, 7th ed., Harlow: Pearson, 864 p.
8. Barabash O.V., Bodrov S.V. and Musienko A.P. (2014), “Analysis of the construction of a network of video surveillance of customs observation posts based on a functionally stable system”. Scientific and practical journal "Communication". Issue 2. P. 8 – 11.
9. Mashkov O.A. and Barabash O.V. (2005), “Assessment of functional stability of distributed information and control systems”. Physical and mathematical modeling and information technology: Collection of scientific works. Lviv, Center for Mathematical Modeling of the Institute of Applied Problems of Mechanics and Mathematics named after Ya.S. Pidstryhach of the NAS of Ukraine. Issue. 1. P. 159 – 165.
10. Ruban, I.V., Martovytskyi, V.O., Kovalenko, A.A. and Lukova-Chuiko, N.V. (2019), “Identification in Informative Systems on the Basis of Users' Behaviour”, Proceedings of the International Conference on Advanced Optoelectronics and Lasers, CAOL 2019-September,9019446, pp. 574-577.
11. Barabash O.V. Durniak B.V., Mashkov O.A., Obidin D.M. (2012), “Ensuring the functional stability of complex technical systems”. Modeling and information technology: Coll. Science. Kyiv, Institute of Modeling Problems in Energy named after G. Pukhov NAS of Ukraine. Is. 64. P. 36 – 41.
12. Chen Y.G. and Feng J. (2012). “Fractal-based exponential distribution of urban density and self-affine fractal forms of cities”, Chaos, Solitons & Fractals, 45(11): 1404-1416.
13. Kuchuk, N., Mozhaiev, O., Mozhaiev, M. and Kuchuk, H. (2017), “Method for calculating of R-learning traffic peakedness”, 2017 4th International Scientific-Practical Conference Problems of Infocommunications Science and Technology, PIC S and T 2017 – Proceedings, pp. 359-362.
14. Vapnik V. (1987), Estimation of Dependences based on Empirical Data, Springer-Verlag, N.-Y., Berlin, 326 p.
15. Semenov, S., Sira, O., Kuchuk, N. (2018), “Development of graphicanalytical models for the software security testing algorithm”, Eastern-European Journal of Enterprise Technologies, Vol 2, No 4 (92), pp. 39-46.
16. Silverman B.W. (1988), “Choosing the window wiolth when estimating a density”, Biotechnika, Vol. 65, No 1, pp. 1 – 11.
17. M. Piccioniab and M. Romaab (1990), “Stochastic regularization of linear equations and the realization of Gaussian fields”, Journal of Multivariate Analysis, Volume 33, Issue 1, April 1990, Pages 143-150.