Abstract
Modern data centers are complex and effective solutions for managing enterprises and corporations, organizing data processing and storage systems, and distributing software applications among available resources. This paper considers load balancing clusters of data centers that contain a definite set of application servers, file servers, data storage systems, and an input-output system, all interconnected by a switching system and communication channels. The goal of research is to increase the efficiency of virtualized data centers using a new methodological approach, as well as rational methods and models, to distribute their load among the available software and hardware means. Interrelated mathematical methods and models, necessary to develop specialized software for the rational planning and allocation of the data center’s physical resources on a given time interval, are proposed. These methods and models are based on the system of correct mappings of the set of request parameters into the known characteristics of the data center’s physical resources. As an optimization criterion, the maximum system performance for a certain time interval is used.
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References
Mochalov, V.P., Linets, G.I., Bratchenko, N.Y., Govorova, S.V.: An analytical model of a corporate software-controlled network switch. Scalable Comput. 21(2), 337–346 (2020)
Boev, V.: Komp’yuternoe modelirovanie: Posobie dlya prakticheskikh zanyatii, kursovogo i diplomnogo proektirovaniya v AnyLogic7 [Computer Modeling: A Manual for Practical Classes, Coursework and Diploma Projects in AnyLogic7], 432p. VAS Publications, St. Petersburg (2014). (In Russian)
Kim, T.-H., Kim, S., Park, H.-U., Kim, M.-S.: Analysis of security session reusing in distribution server system. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3984, pp. 700–707. Springer, Heidelberg (2006). https://doi.org/10.1007/11751649_77
Khritankov, A.: Modeli i algoritmy raspredeleniya nagruzki. Algoritmy na osnove setei SMO [Models and Algorithms for Load Balancing. Algorithms Based on Networks of Queuing Systems]. Informatsionnye tekhnologii i vychislitel’nye seti – Information Technologies and Computer Networks, vol. 3 (2009). (In Russian)
Ivanisenko, I., Kirichenko, L., Radivilova, T.: Metody balansirovki s uchetom multifraktal’nyh svoistv nagruzki [Balancing Methods Considering Multifractal Properties of Load]. Int. J. Inf. Content Process. 2(4), 345–368 (2015). (In Russian)
Panchenko T.V.: Genetic Algorithms [Text]: A Manual/Yu.Yu. Tarasevich. Astrakhan University, Astrakhan, 87 p. (2007). (In Russian)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, 211 p. J.H. Holland, The MIT Press, Cambridge (1992)
Michalewicz, Z.: Genetic algorithms + Data Structures = Evolution Programs, 387 p. Springer, New York (1996). https://doi.org/10.1007/978-3-662-03315-9
Tsoi Yu.R., Spitsyn, V.G.: Genetic Algorithm. In: Knowledge Representation in Information Systems: A Manual, 146 p. Tomsk Polytechnic University, Tomsk (2006). (In Russian)
Mitchell, M.: An Introduction to Genetic Algorithms, 158 p. MIT Press, Cambridge (1999)
Periaux, J., Sefrioui, M.: Evolutionary computational methods for complex design in aerodynamics. In: AIAA-98-0222, 15 p., Reno (1998)
Periaux, J., Chen, H.Q., Mantel, B., Sefrioui, M., Sui, H.T.: Combining game theory and genetic algorithms with application to DDM-nozzle optimization problems. Finite Elem. Anal. Des. 37(5), 417–429 (2001)
Zhirkov, A.: Supercomputers: Development, trends, usage. Eurotech HPC solutions review [Superkomp’yutery: razvitie, tendentsii, primenenie. Obzor HPC-reshenii Eurotech]. Present Automation Solutions, no. 2, p. 201 (2014)
Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. Pract. Exp. (CCPE) 24(13), 1397–1420 (2012)
Mochalov, V.P., Bratchenko, N.Y., Yakovlev, S.V.: Analytical model of object request broker based on CORBA standard. J. Phys. Conf. Ser. 1015(2), 022012 (2018). https://doi.org/10.1088/1742-6596/1015/2/022012
Mochalov, V.P., Bratchenko, N.Y., Yakovlev, S.V.: Analytical model of integration system for program components of distributed object applications. In: International Russian Automation Conference (RusAutoCon 2018), no. 8501806 (2018). https://doi.org/10.1109/RUSAUTOCON.2018.8501806
Mochalov, V., Bratchenko, N., Linets, G., Yakovlev, S.: Distributed management systems for infocommunication networks: a model based on TM Forum Frameworx. Computers 8(2), 45 (2019). https://doi.org/10.3390/computers8020045
Mochalov, V.P., Bratchenko, N.Y., Yakovlev, S.V.: Process-oriented management system for infocommunication networks and services based on TM forum Frameworx. In: Proceedings of 2019 International Russian Automation Conference (RusAutoCon 2019), no. 8867619 (2019).https://doi.org/10.1109/RUSAUTOCON.2019.8867619
Vdovin, P.M., Kostenko, V.A.: Algorithm for resource allocation in data centers with independent schedulers for different types of resources. J. Comput. Syst. Sci. Int. 53(6.1), 854–867 (2014)
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This work was supported by the Russian Foundation for Basic Research, project no. 19-07-00856/20.
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Mochalov, V.P., Linets, G.I., Palkanov, I.S. (2021). Methods and Models of Resource Allocation in Load Balancing Clusters. In: Silhavy, R. (eds) Software Engineering and Algorithms. CSOC 2021. Lecture Notes in Networks and Systems, vol 230. Springer, Cham. https://doi.org/10.1007/978-3-030-77442-4_47
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