Building the Software-Defined Data Center

Abstract

Data center is the most effective way of providing computational resources to a large number of users. The software-defined model is a modern approach to the creation of the computing infrastructure for the data center, which allows user tasks to be processed in acceptable time and at acceptable cost. This paper formulates the general design requirements for the interagency data center and describes some problems and methods of planning and building software-defined data centers (deployment of computing systems optimized for maximum hardware utilization, software support for different classes of tasks, etc.).

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REFERENCES

  1. 1

    Hellerstein, J.M., Programming a parallel future, Technical report no. UCB/EECS-2008-144, EECS Department, University of California, Berkeley, 2008.

  2. 2

    Asanović, K., Bodik, R., Catanzaro, B.C., Gebis, J.J., Husbands, P., Keutzer, K., Patterson, D.A., Plishker, W.L., Shalf, J., Williams, S.W., and Yelick, K.A., The landscape of parallel computing research: A view from Berkeley, Technical report no. UCB/EECS-2006-183, EECS Department, University of California, Berkeley, 2006.

  3. 3

    Perks, M., Reference architecture: VMware software defined data center. https://lenovopress.com/lp0661-reference-architecture-vmware-software-defined-data-center-thinkagile-vx. Accessed August 15, 2018.

  4. 4

    Fujitsu, Software-defined data center: Infrastructure for enterprise digital transformation. https://sp.ts.fujitsu.com/dmsp/Publications/public/wp-sddc-infrastructure-for-enterprise-digital-transformation-ww-en.pdf.

  5. 5

    EMC Reference Architecture, Federation software-defined data center. http://www.emc.com/collateral/ TechnicalDocument/h13378-evp-sddc-ra.pdf.

  6. 6

    Gaisaryan, S., Samovarov, O., Avetisyan, A., and Ivannikov, V., University cluster: integration of education, science, and industry, Otkrytye Sist. SUBD, 2010, no. 5.

  7. 7

    Avetisyan, A., Samovarov, O., Gaisaryan, S., and Khashba, E., OpenCirrus: Russian segment, Otkrytye Sist. SUBD, 2011, no. 5.

  8. 8

    Samovarov, O., Kraposhin, M., and Strizhak, S., Web laboratory UniHUB in the framework of the program “University cluster,” Proc. Workshop Multiphysical Modelling in OpenFOAM, Riga, 2011.

  9. 9

    Samovarov, O. and Strizhak, S., Features of the implementation of the web laboratory of continuum mechanics on the basis of the technological platform of the program “University cluster,” Trudy mezhdunarodnoi superkomp’yuternoi konferentsii s elementami nauchnoi shkoly dlya molodezhi “Nauchnyi servis v seti Internet: ekzaflopsnoe budushchee” (Proc. Int. Supercomputer Conference with Elements of Scientific School for Young People “Scientific Service on the Internet: An Exaflop Future”), Izd. Mosk. Gos. Univ., 2011.

  10. 10

    Kraposhin, M.V., Samovarov, O.I., and Strizhak, S.V., Experience of using free software for computing industrial-scale spatial hydrodynamics, Tr. konf. Svobodnoe programmnoe obespechenie (Proc. Conf. Free Software), Izd. S.-Peterb. Gos. Politekh. Univ., 2010.

  11. 11

    Tchernykh, A., Miranda-López, V., Babenko, M., Armenta-Cano, F., Radchenko, G., Drozdov, A.Y., and Avetisyan, A., Performance evaluation of secret sharing schemes with data recovery in secured and reliable heterogeneous multi-cloud storage, Cluster Comput. (in press). https://doi.org/10.1007/s10586-018-02896-9

  12. 12

    Turchaninov, V.Y., Kosenkov, S.O., Samovarov, O.I., Tchij, O.P., Korovin, I.S., and Schaefer, G., High-performance cloud computing for managing the life cycle of oil and gas fields, Adv. Intell. Syst. Comput., 2019, pp. 1093–1098. https://doi.org/10.1007/978-981-10-8944-2_127

  13. 13

    Tchernykh, A., Babenko, M., Chervyakov, N., Miranda-López, V., Kuchukov, V., Cortés-Mendoza, J.M., Deryabin, M., Kucherov, N., Radchenko, G., and Avetisyan, A., AC-RRNS: Anti-collusion secured data sharing scheme for cloud storage, Int. J. Approximate Reasoning, 2018, pp. 60–73. https://doi.org/10.1016/j.ijar.2018.07.010

    Google Scholar 

  14. 14

    Feoktistov, A., Sidorov, I., Tchernykh, A., Edelev, A., Zorkalzev, V., Kostromin, R., Gorsky, S., Bychkov, I., and Avetisyan, A., Multi-agent approach for dynamic elasticity of virtual machines provisioning in heterogeneous distributed computing environment, Proc. Int. Conf. High Performance Computing and Simulation (HPCS), 2018. https://doi.org/10.1109/HPCS.2018.00145

  15. 15

    Borisenko, O.D. and Lazarev, N.A., Implementing JSON operations for inmemory data grid as pass-through cache layer to RDBMS, Int. J. Civ. Eng. Technol., 2018, vol. 9, no. 10, pp. 1033–1040.

    Google Scholar 

  16. 16

    Tchernykh, A., Babenko, M., Miranda-Lopez, V., Drozdov, A.Y., and Avetisyan, A., WA-RRNS: Reliable data storage system based on multi-cloud, Proc. 32nd IEEE Int. Parallel and Distributed Processing Symp. Workshops (IPDPSW), 2018. https://doi.org/10.1109/IPDPSW.2018.00107

  17. 17

    Massobrio, R., Nesmachnow, S., Tchernykh, A., Avetisyan, A., and Radchenko, G., Towards a cloud computing paradigm for big data analysis in smart cities, Program. Comput. Software, 2018, vol. 44, no. 3, pp. 181–189. https://doi.org/10.1134/S0361768818030052

    Article  Google Scholar 

  18. 18

    Lopez-Falcon, E., Tchernykh, A., Chervyakov, N., Babenko, M., Nepretimova, E., Miranda-López, V., Drozdov, A.Y., Radchenko, G., and Avetisyan, A., Adaptive encrypted cloud storage model, Proc. IEEE Conf. Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), 2018, pp. 329–334. https://doi.org/10.1109/EIConRus.2018.8317099

  19. 19

    Tchernykh, A., Babenko, M., Chervyakov, N., Miranda-Lopez, V., Cortes-Mendoza, J.M., Du, Z., Nava-ux, P.O.A., and Avetisyan, A., Analysis of secured distributed cloud data storage based on multilevel RNS, Proc. IEEE Conf. Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), 2018, pp. 382–386. https://doi.org/10.1109/EIConRus.2018.8317112

  20. 20

    Kalugin, M.D., Korchagova, V.N., Kraposhin, M.V., Marchevsky, I.K., and Moreva, V.S., Using big analytics tools in performance of gas dynamics and acoustics tasks, Herald Bauman Moscow State Tech. Univ. Ser. Nat. Sci., no. 3, pp. 32–47. https://doi.org/10.18698/1812-3368-2018-3-32-47

  21. 21

    Canosa, R., Tchernykh, A., Cortés-Mendoza, J.M., Rivera-Rodriguez, R., Rizk, J.L., Avetisyan, A., Du, Z., Radchenko, G., and Morales, E.R.C., Energy consumption and quality of service optimization in containerized cloud computing, Proc. Inst. Syst. Program. Russ. Acad. Sci., 2018, pp. 47–55. https://doi.org/10.1109/ISPRAS.2018.00014

  22. 22

    Varnovsky, N.P., Zakharov, V.A., and Shokurov, A.V., On the existence of provably secure cloud computing systems, Moscow Univ. Comput. Math. Cybernet., 2016, vol. 40, no. 2, pp. 83–88. https://doi.org/10.3103/S0278641916020096

    MathSciNet  Article  MATH  Google Scholar 

  23. 23

    Varnovskiy, N.P., Martishin, S.A., Khrapchenko, M.V., and Shokurov, A.V., Secure cloud computing based on threshold homomorphic encryption, Program. Comput. Software, 2015, vol. 41, no. 4, pp. 215–218. https://doi.org/10.1134/S0361768815040088

    MathSciNet  Article  MATH  Google Scholar 

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Correspondence to B. M. Shabanov or O. I. Samovarov.

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Translated by Yu. Kornienko

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Shabanov, B.M., Samovarov, O.I. Building the Software-Defined Data Center. Program Comput Soft 45, 458–466 (2019). https://doi.org/10.1134/S0361768819080048

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