RESOURCES DISTRIBUTION MODEL OF CRITICAL IT INFRASTRUCTURE WITH CLEAR PARAMETERS BASED ON THE PARTICLE SWARM ALGORITHM

Y.Y. Dorogyy, O.O. Doroha-Ivaniuk, D.A. Ferens

Èlektron. model. 2018, 41(2):23-38
https://doi.org/10.15407/emodel.41.02.023

ABSTRACT

A detailed analysis of the methods and algorithms for allocating resources for virtualized IT infrastructuresA detailed analysis of the methods and algorithms for allocating resources for virtualized IT infrastructureshas been carried out. A detailed description of the mathematical model of resourceallocation of a critical IT infrastructure with clear parameters and its use in combination with theparticle swarm method is given. The method of particle swarm, the principle of finding the best solution and its basic operations for solving a given problem are disclosed. The last part of the articlesolution and its basic operations for solving a given problem are disclosed. The last part of the articlepresents experimental researches of the proposed model of distribution of critical IT infrastructureresources with clear parameters based on the particle swarm method.

KEYWORDS

architecture, resource allocation, particle swarm method, critical IT infrastructure.

REFERENCES

1. Globa, L.S., Skulish, M.A. and Dyadenko, O.M. (2007), Matematicheskie osnovy postroeniya informatsionno-telekommunikatsionnykh sistem [Mathematical Foundations of Construction of Information and Telecommunication Systems], Norita-plus, Kyiv, Ukraine.
2. Gorin, M. (2007), “Corporate Data Center: Beyond Technology”, Connect! Mir Svjazi, no. 8, pp. 19-20.
3. Kirillov, I. (2010), “Commercial data centers in Ukraine: a new stage of development”, Seti i biznes, no. 3, available at: http://www.sib.com.ua/arhiv 2010/2010_3/statia_3_1_2010/statia_3_1_2010.htm (accessed December 10, 2018).
4. Badger, L., Grance, T., Patt-Corner, R. and VoasCloud, J. (2012), “Computing Synopsis and Recommendations. Recommendations of the National Institute of Standards and Technology”, Special Publication.
https://doi.org/10.6028/NIST.SP.800-146
5. Bibershtein, N. and Bouz, S. (2007), Kompas v mire servis-orientirovannoy arhitektury [Compass in the world of service-oriented architecture], KUDITs-Press, Moscow, Russia.
6. Krafzik, D., Banke, K. and Slama, D. (2005), Enterprise SOA: Service-Oriented Architecture Best Practices, Prentice Hall Professional.
7. Kaur, R. and Kaur, A. (2014), “A Review Paper on Evolution of Cloud Computing, its Approaches and Comparison with Grid Computing”, International Journal of Computer Science and Information Technologies, Vol. 5, pp. 6060-6063.
8. Gupta, A., Sarood, O. and Kale, L.V. (2014), “Optimizing VM Placement for HPC in the Cloud”, International Letters of Social and Humanistic Sciences, Vol. 16, pp. 1-6.
9. Joseph, J. and Fellenstein, C. (2004), Grid Computing, Prentice Hall Professional.
10. Nabrzyski, J., Schopf, J.M. and Weglarz, J. (2004), Grid Resource Management: State of the Art and Future Trends, Springer.
https://doi.org/10.1007/978-1-4615-0509-9
11. Goldworm, B. and Skamarock, A. (2007), Blade servers and virtualization: transforming enterprise computing while cutting costs, Wiley Publishing, Inc.
12. Ruest, N. and Ruest, D. (2009), Virtualization, A Beginner’s Guide, McGraw Hill Professional.
13. Telenik, S.F., Rolik, O.I., Bukasov, M.M. and Labunskiy, A.Yu. (2009), “Virtual Machine Management Models for Server Virtualization”, Visnik NTUU «KPI»: Informatika, upravlennya ta obchislyuvalna tekhnika, no. 51, pp. 147-152.
14. Dorogyy, Ya.Yu., Doroga-Ivanyuk, O.O. and Ferens, D.A. (2018), “Critical IT infrastructure resource allocation model with clear parameters based on genetic algorithm”, Information technology and security, Vol. 6, no. 2(11), pp. 124-144.
https://doi.org/10.20535/2411-1031.2018.6.2.153497
15. Buyya, R., Broberg, J. and Goscinski, A.M. (2010), Cloud Computing: Principles and Paradigms, John Wiley & Sons.
https://doi.org/10.1002/9780470940105
16. Hu, X. and Eberhart, R.C. (2001), “Tracking dynamic systems with PSO: Where’s the cheese”, Proceedings of the Workshop on Particle Swarm Optimization, IN, pp. 80-83.
17. Shi, Y.H. and Eberhart, R. C. (1999), “Empirical study of particle swarm optimization”, Proceedings17. Shi, Y.H. and Eberhart, R. C. (1999), “Empirical study of particle swarm optimization”, Proceedingsof the Congress on Evolutionary Computation, Vol. 3, pp. 1945-1950.
https://doi.org/10.1109/CEC.1999.785511
18. Eberhart, R.C. and Shi, Y. (2001), “Tracking and optimizing dynamic systems with particleswarms”, Proceedings of the 2001 Congress on Evolutionary Computation, Vol. 1, pp. 94-100. DOI: https://doi.org/10.1109/CEC.2001.934376.
19. Carlisle, A. and Dozler, G. (2002), “Tracking changing extrema with adaptive particleswarm optimizer”, Proceedings of the 2002 Soft Computing, Multimedia Biomedicine, ImageProcessing and Financial Engineering, Orlando, FL, USA, pp. 265-270.
20. Du, W., and Li, B. (2008). “Multi-strategy ensemble particle swarm optimization for dynamicoptimization”, International Journal of Information Sciences, Vol. 178, no. 15, pp. 3096-3109. DOI:
https://doi.org/10.1016/j.ins.2008.01.020.
21. Tasgetiren, M.F., Sevkli, M., Liang, Y.-C. and Gencyilmaz, G. (2004), “Particle swarm optimizationalgorithm for permutation flow shop sequencing problem”, Proceedings of the 4thInternational Workshop on Ant Colony, Optimization, and Swarm Intelligence (ANTS2004),Brussels, Belgium, pp. 382-390.
https://doi.org/10.1007/978-3-540-28646-2_38
22. Tasgetiren, M.F., Sevkli, M., Liang, Y.-C. and Gencyilmaz, G. (2006), “Particle swarm optimizationalgorithm for single-machine total weighted tardiness problem”, Proceedings ofthe Congress on Evolutionary Computation, Vol. 2, pp. 1412-1419.
23. Van der Maaten, L.J.P. and Hinton, G.E. (2008), “Visualizing High-Dimensional Data Usingt-SNE”, Journal of Machine Learning Research, Vol. 9, pp. 2579-2605.

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