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MATHEMATICAL MODEL OF THE LOAD BALANCING SYSTEM OF DPC SERVER CLUSTERS UNDER FRACTAL LOAD CONDITIONS

https://doi.org/10.37493/2307-910X.2022.4.4

Abstract

A mathematical model of the system for distributing and balancing the load of servers of clusters of data processing centers (DPC) is proposed, which provides a solution to the problem of assessing its performance, taking into account the degree of workload. The performance of the proposed model and the verification of the results obtained were carried out by simulation. The characteristics of the average queue length, average delay, and packet loss probability were used as the main quality indicators. The mathematical apparatus for evaluating these quality indicators is the queuing theory. The load distribution and balancing system is presented as a multi-channel system with a limit on the length of the queue, which includes an unlimited buffer (disk memory) for all servers in the cluster, as well as input buffers of limited capacity for each server. The model is built taking into account the features of the network traffic of modern infocommunication networks, characterized by self-similarity properties, and each type of traffic (HTTP/TCP, HTTPS. SMTP/TCP, VoIP, FTP/TCP, IP, Ethernet, ATM) is described only by its characteristic distribution law as packet arrival intervals and protocol block lengths. To take into account the features of the self-similar network traffic entering the system, it is described by the fractal Brownian motion fBM/M/1/N and a special function that depends on the self-similarity coefficient H (Hurst coefficient). The presented model can also be used to study the characteristics of network traffic in order to prevent network congestion and minimize losses.

About the Authors

V. P. Mochalov
North Caucasus Federal University
Russian Federation

Mochalov Valeriy P., Doctor of Technical Sciences, Professor, Institute of Digital Development, Department of Infocommunications

Stavropol



N. Yu. Bratchenko
North Caucasus Federal University
Russian Federation

Bratchenko Natalia Yu., Candidate of Physical and Mathematical Sciences, Associate Professor, Institute of Digital Development, Department of Infocommunications

Stavropol



I. S. Palkanov
North Caucasus Federal University
Russian Federation

Palkanov Ilya S., programmer, Institute of Digital Development, Department of Infocommunications

Stavropol



E. V. Aliev
North Caucasus Federal University
Russian Federation

Aliev Eldar V., graduate student, Institute of Digital Development, Department of Infocommunications

Stavropol



References

1. Computing Center of the Institute of High Energy Physics (IHEP-CC) (2016). ―VCondor – virtual computing resource pool manager based on HTCondor‖. Retrieved from https://github.com/hep-gnu/VCondor.

2. McNab A., Love P., and MacMahon E. (2015). Managing virtual machines with Vac and Vcycle, J. Phys.: Conf. Ser., Vol. 664.

3. Feller E., Rilling L., and Morin C. Snooze (2012). A scalable and autonomic virtual machine management framework for private Clouds, Proceedings of the 12th IEEE/ACMInternational Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 482- 489.

4. Beloglazov, R. Buyya (2015). OpenStack Neat: A Framework for Dynamic and Energy-Efficient Consolidation of Virtual Machines in OpenStack Clouds, Concurrency and Computation: Practice and Experience (CCPE), Vol. 27, No. 5, pp. 1310-1333.

5. Anne-C´ecile Orgerie, Laurent Lef`ever (2009). When Clouds become Green: The Green Open Cloud Architecture, International Conference on Parallel Computing (ParCo), pp. 228- 237.

6. Ward J.S., Barker A. (2014). Observing the clouds: a survey and taxonomy of cloud monitoring, Jour-nal of Cloud Computing: Advances, Systems and Applications, Vol. 3.

7. Ward J.S., Barker A. (2015). Cloud cover: monitoring large-scale clouds with Varanus, Journal of Cloud Computing: Advances, Systems and Applications, Vol. 4.

8. Open Grid Forum (2016). ―Open Cloud Computing Interface‖. Retrieved from http://occiwg.org/.

9. Baldoni M., Baroglio M., Martelli A., ―Verifying the conformance of web servicesto global interaction protocols: A first step‖, International Workshop on WebServices and Formal Methods, 2005, P. 27.

10. Egawa T., ―SDN standardization Landscape fromITU-T Study Group 13 / T. Egawa,‖ ITU Workshop on SDNGeneva, Switzerland, 4 June 2013.

11. Mochalov V.P., Bratchenko N.Y., Yakovlev S.V., ―Analytical model of object request broker based on Corba standard‖ (2018) Journal of Physics: Conference Series, 1015 (2). doi: 10.1088/1742-6596/1015/2/022012.

12. A. Vishnu Priya; N. Radhika Performance comparison of SDN OpenFlow controllers // International Journal of Computer Aided Engineering and Technology, 2019, Vol.11 No.4/5, pp.467 – 479.

13. Nageswara S. V. Rao Performance Comparison of SDN Solutions for Switching Dedicated Long-Haul Connections // ICN 2016: The Fifteenth International Conference on Networks. pp. 110-117.

14. Idris Z. Bholebawa, Upena D. Dalal Performance Analysis of SDN/OpenFlow Controllers: POX Versus Floodlight // Wireless Personal Communications. January 2018, Volume 98, Issue 2, pp 1679–1699. https://doi.org/10.1007/s11277-017-4939-z.

15. Tong Li, Jinqiang Chen, Hongyong Fu Application Scenarios based on SDN: An Overview // IOP Publishing. IOP Conf. Series: Journal of Physics: Conf. Series 1187 (2019) 052067 doi:10.1088/1742-6596/1187/5/052067.

16. Boev V. Kompjuternoe modelirovanie: Posobie dlja prakticheskih zanjatij, kursovogo i diplomnogo proektirovanija v AnyLogic7 [Computer modeling: A manual for practical classes, course and diploma projects in AnyLogic7] St. Petersburg, VAS Publ., 2014, 432p. (In Russian)

17. Taihoon K., Soksoo K. Analysis of Security Session Reusing in Distribution Server System. Computational Science and Its Applications - ICCSA 2006. Springer, 2006, 1045 p.

18. Khritankov A. Modeli i algoritmy raspredelenija nagruzki. Algoritmy na osnove setej SMO [Models and algorithms of load balancing. Algorithms on the basis of networks of queuing systems]. Informacionnye tehnologii i vychislitel'nye seti – Information technologies and computer networks. 2009, vol. 3. (In Russian)

19. Ivanisenko I., Kirichenko L., Radivilova T. Metody balansirovki s uchetom multifraktal'nyh svojstv nagruzki [Balancer multifractal methods considering load characteristics]. International Journal "Information Contentand Processing". 2015, vol. 2, no. 4, pp. 345–368. (In Russian)

20. Рanchenko T.V. Genetic Algorithms [Text]: teaching aid /Yu.Yu. Tarasevich. Astrakhan: «Astrakhanskiy Universitet»,2007. – 87 p. (In Russian)


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For citations:


Mochalov V.P., Bratchenko N.Yu., Palkanov I.S., Aliev E.V. MATHEMATICAL MODEL OF THE LOAD BALANCING SYSTEM OF DPC SERVER CLUSTERS UNDER FRACTAL LOAD CONDITIONS. Modern Science and Innovations. 2022;(4):41-49. https://doi.org/10.37493/2307-910X.2022.4.4

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