Nikiforov
Vladimir O.
D.Sc., Prof.
doi: 10.17586/2226-1494-2019-19-2-271-279
MODEL OF MULTI-LEVEL DATA STORAGE SYSTEM
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Tatarnikova T.M., PoymanovaE.D. Model of multi-level data storage system. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, vol. 19, no. 2, pp. 271–279 (in Russian). doi: 10.17586/2226-1494-2019-19-2-271-279
Abstract
A model of multi-level organization for data storage system is studied, based on the sequential application of algorithms for vertical file distribution by the levels of data storage system, horizontal placement in sections of a certain level, and dynamic placement as a result of data migration. Selection and normalization of metadata specifying the characteristics of the stored files were performed. The model of multi-level data storage provides the storage of files in accordance with their characteristics and meets the requirements for guaranteed storage time. Representation of the storage system in the form of a matrix enables the usage of Kohonen neural network tool to arrange files by levels and sections of a specific storage system level. The application of Kohonen neural network provides the transfer from sequential execution of algorithms to placement in one step. We have proposed the model of multi-level data storage. Algorithms have been developed for file placement in a multi-level data storage system. Test examples are given which demonstrate the ability of Kohonen neural network apparatus as a tool for solving the file allocation problem in accordance with the required parameters. The combined use of file allocation algorithms gives the possibility to organize multi-level data storage in accordance with the files characteristics and the assurance of time requirements for guaranteed storage.
References
-
Bogatyrev V.A., Bogatyrev S.V. Association reservation servers in clasters highly reliable computer system. Informatsionnye Tekhnologii, 2009, no. 6, pp. 41–47. (in Russian)
-
Proskuryakov N.E., Anufrieva A.Y. Analysis and prospects of modern systems of storage of figures. News of the Tula State University. Technical Sciences, 2013, no. 3, pp. 368–377.
(in Russian) -
Burmistrov V.D., Zakovryashin E.M. Creating a data warehouse for a distributed system. Molodoi Uchenyi, 2016, no. 12, pp. 143–147. (in Russian)
-
Bogatyrev V.A., Parshutina S.A., Poptcova N.A., Bogatyrev A.V. Efficiency of redundant service with destruction of expired and irrelevant request copies in real-time clusters. Communications in Computer and Information Science, 2016, vol. 678, pp. 337–348. doi: 10.1007/978-3-319-51917-3_30
-
Farley M. Building Storage Networks. 2nd ed. Osborne, McGraw-Hall, 2001, 576 p.
-
Bogatyrev V.A., Bogatyrev S.V., Bogatyrev A.V. Reliability clusters computing systems with the duplicated communications of servers and storage devices. Information Technologies, 2013, no. 2, pp. 27–32. (in Russian)
-
Information Storage and Management. 2nd ed. New Jersey, John Wiley & Sons Inc., 2016, 544 p.
-
LandauerR.Information is physical. Physics Today, 1991, vol. 44, no. 5, pp. 23-29.
-
Todman C. Designing a Data Warehouse: Supporting Customer Relationship Management. Prentice Hall Publ.,2001, 414 p.
-
Kish L.B., Granqvist C.G. Does information have mass? Proceedings of the IEEE, 2013, vol. 101,no. 9, pp. 1895–1899. doi: 10.1109/JPROC.2013.2273720
-
Mesnier M., Ganger G., Riedel E. Object-based storage. IEEE Communications Magazine, 2003, vol. 41, no. 8, pp. 84–90.doi: 10.1109/MCOM.2003.1222722
-
Buyya R., Broberg J., Goscinski A. Cloud Computing. Principles and Paradigms. New Jersey, John Wiley & Sons Inc., 2011, 637 p.
-
Tatarnikova T.M., Poimanova E.D. Long term storage technologies. Proc. Int. Conf. on Science and Education in the 21st Century. Tambov, Russia, 2013, part 31, pp. 136–138.
(in Russian) -
Tatarnikova T.M. Data Analysis. St. Petersburg, SPbSEU Publ., 2018, 85 p. (in Russian)
-
Kish L.B. Moore’s law and the energy requirement of computing versus performance. IEE Proceedings: Circuits, Devices
and Systems, 2004, vol. 151, no. 2, pp. 190–194. doi:
10.1049/ip-cds:20040434 -
Hastic T., Tibshirani R., Friedman J. The Elements of Statistical Learning. Springer, 2003, 552 p. doi: 10.1007/978-0-387-21606-5
-
Kohonen T.Self-Organizing Maps. NY, Springer, 1995, 362 p.
-
Stacey M., Salvatore J., Jorgensen A. Visual Intelligence: Microsoft Tools and Techniques for Visualizing Data. New Jersey: Wiley, 2013, 400 p.
-
Morville P., Callender J. Search Patterns: Design for Discovery. O'Reilly Media, 2010, 192 p.
-
Kutuzov O.I., Tatarnikova T.M. Model of a self-similar traffic generator and evaluation of buffer storage for classical and fractal queuing system. Proc. Moscow Workshop on Electronic and Networking Technologies, MWENT. Moscow, 2018. doi: 10.1109/mwent.2018.8337306
-
Kutuzov O.I., Tatarnikova T.M. Practical experience of using Monte Carlo method. Industrial Laboratory, 2017, vol. 83, no. 3, pp. 65–70. (in Russian)