Development of Components of a Distributed Fault Tolerant Medical Data Storage System

Development of Components of a Distributed Fault Tolerant Medical Data Storage System

  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-12
Year of Publication : 2022
Author : Aslan A. Tatarkanov, Abas Kh. Lampezhev, Dmitry V. Polezhaev, Ruslan Kh. Tekeev
DOI : 10.14445/22315381/IJETT-V70I12P209

How to Cite?

Aslan A. Tatarkanov, Abas Kh. Lampezhev, Dmitry V. Polezhaev, Ruslan Kh. Tekeev, " Development of Components of a Distributed Fault Tolerant Medical Data Storage System," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 76-89, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P209

Abstract
In human activities, various technologies based on a distributed approach are increasingly mastered; they are designed to ensure efficient and reliable information storage, quick data access, and the possibility of implementing parallelism when working with them. Comprehensive research aimed at optimizing the tactical, technical, and economic indicators determining the appearance and functionality of such storage systems is an urgent scientific problem. One of its parts is the need to investigate the possibilities of developing components of an effective fault-tolerant medical data storage system. This determined the article's topic relevance. This research aimed to develop new mathematical models and algorithms, based on known alternative technologies, for the basic components of a distributed storage system that can be effectively implemented. The application of these components will increase system fault tolerance using controlled redundancy. The article shows that, among the possible options for a distributed fault-tolerant data storage system structure, distributed data storage systems are most promising in ensuring efficient and reliable information storage. Moreover, this refers to such systems whose mechanism of maintaining reliability (fault tolerance) is based on the operation of the errorcorrecting code based on a redundant residue number system. A model of a distributed data storage system with a redundancy function is proposed. It is substantiated that the latter's functioning depends on the efficiency and practical applicability of the approaches to converting values from a positional system to a redundant one.

Keywords
Algebraic codes, Data errors, Data redundancy, Data storage system, Non-positional notation.

References
[1] Conor JohnCremin et al., “Big Data: Historic Advances and Emerging Trends in Biomedical Research,” Current Research in Biotechnology, vol. 4, pp. 138–151, 2022. Crossref, https://doi.org/10.1016/j.crbiot.2022.02.004
[2] Manar Sais, Najat Rafalia, and Jaafar Abouchabaka, “Intelligent Approaches to Optimizing Big Data Storage and Management: REHDFS System and DNA Storage,” Procedia Computer Science, vol. 201, pp. 746–751, 2022. Crossref, https://doi.org/10.1016/j.procs.2022.03.101
[3] Sarah Wordsworth et al., “Using “Big Data” in the Cost-Effectiveness Analysis of Next-Generation Sequencing Technologies: Challenges and Potential Solutions,” Value in Health, vol. 21, no. 9, pp. 1048–1053, 2018. Crossref, https://doi.org/10.1016/j.jval.2018.06.016
[4] Abas H. Lampezhev, Islam A. Alexandrov, and Victor A. Gorelov, “Automated Analysis of Big Data From Social Networks as a Way to Compile a Psychological Portrait of a Personality,” Proceedings of the International Conference on Quality Management, Transport and Information Security, Information Technologies, pp. 511-515, 2021. Crossref, https://doi.org/10.1109/ITQMIS53292.2021.9642901
[5] NataliyaShakhovska et al., “Big Data Processing Technologies in Distributed Information Systems,” Procedia Computer Science, vol.160, pp. 561–566, 2019. Crossref, https://doi.org/10.1016/j.procs.2019.11.047
[6] Mohammad S. Aslanpour, Sukhpal Singh Gill, and Adel N. Toosi, “Performance Evaluation Metrics for Cloud, Fog and Edge Computing: A Review, Taxonomy, Benchmarks and Standards for Future Research,” Internet of Things, vol.12, 100273, 2020. Crossref, https://doi.org/10.1016/j.iot.2020.100273
[7] Dalia Kamal A. A. Rizk et al., “Applying Ai for Timely Input to a Smart Healthcare System,” Journal of Southwest Jiaotong University, vol. 57, no. 4, pp. 312–325, 2022. Crossref, https://doi.org/10.35741/issn.0258-2724.57.4.28
[8] HishamAl-Ward, Chee Keong Tan, and Wern Han Lim, “Caching Transient Data in Information-Centric Internet-of-Things (IC-IoT) Networks: A Survey,” Journal of Network and Computer Applications, vol. 206, p. 103491, 2022.
[9] Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller, “Methods for Interpreting and Understanding Deep Neural Networks,” Digital Signal Processing, vol. 73, pp. 1–15, 2018. Crossref, https://doi.org/10.1016/j.dsp.2017.10.011
[10] YongliangXu et al., “Secure Fuzzy Identity-Based Public Verification for Cloud Storage,” Journal of Systems Architecture, vol.128, p. 102558, 2022. Crossref, https://doi.org/10.1016/j.sysarc.2022.102558
[11] MuntadherSaadoon et al., “Fault Tolerance in Big Data Storage and Processing Systems: A Review on Challenges and Solutions,” Ain Shams Engineering Journal, vol. 13, no. 2, p. 101538, 2022.
[12] Aslan Tatarkanov et al., “Suboptimal Biomedical Diagnostics in the Presence of Random Perturbations in the Data,” International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 129–137, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P213
[13] Anan Zhou, Benshun Yi, and Laigan Luo, “Tree-Structured Data Placement Scheme with Cluster-Aided Top-Down Transmission in Erasure-Coded Distributed Storage Systems,” Computer Networks, vol. 204, p. 108714, 2022. Crossref, https://doi.org/10.1016/j.comnet.2021.108714
[14] Islam Alexandrov et al., “Development of Algorithm for Calculating Data Packet Transmission Delay in Software-Defined Networks,” Emerging Science Journal, vol. 6, no. 5, pp. 1062–1074, 2022. Crossref, https://doi.org/10.28991/ESJ-2022-06-05-010
[15] Wenqi Cao, and Cong Zhang, “An Effective Parallel Integrated Neural Network System for Industrial Data Prediction,” Applied Soft Computing, vol. 107, p. 107397, 2021. Crossref, https://doi.org/10.1016/j.asoc.2021.107397
[16] M. Szymczyk, and P. Szymczyk, “Automatic Processing of Z-Transform Artificial Neural Networks Using Parallel Programming,” Neurocomputing, vol. 379, pp. 74–88, 2020. Crossref, https://doi.org/10.1016/j.neucom.2019.10.078
[17] BinLiao et al., “Energy-Efficient Algorithms for Distributed Storage System Based on Block Storage Structure Reconfiguration,” Journal of Network and Computer Applications, vol. 48, no. 1, pp. 71–86, 2015. Crossref, https://doi.org/10.1016/j.jnca.2014.10.008
[18] Yijie Wang, and Sijun Li, “Research and Performance Evaluation of Data Replication Technology in Distributed Storage Systems,” Computers & Mathematics with Applications, vol. 51, no. 11, pp. 1625–1632, 2006. Crossref, https://doi.org/10.1016/j.camwa.2006.05.002
[19] E. V. Sokolov, and E. V. Kostyrin, “Breakthrough Technologies Financing of Development of Science Researches and Competitive Medical Equipment,” AIP Conference Proceedings, vol. 2250, p. 020026, 2020. Crossref, https://doi.org/10.1063/5.0013333
[20] Quanlu Zhang et al., “Ustore: A Low Cost Cold and Archival Data Storage System for Data Centers,” Proceedings of the 35th International Conference on Distributed Computing Systems, pp. 431–441, 2015. Crossref, https://doi.org/10.1109/ICDCS.2015.51
[21] Jianjiang Li et al, “A Data-Check Based Distributed Storage Model for Storing Hot Temporary Data,” Future Generation Computer Systems, vol. 73, pp. 13–21, 2017. Crossref, https://doi.org/10.1016/j.future.2017.03.019
[22] Alexander Thomasian, “Storage Systems. Organization, Performance, Coding, Reliability, and Their Data Processing,” Burlington, Vermont, United States: Morgan Kaufmann, pp. 89-196, 2022.
[23] Lluis Pamies-Juarez, Anwitaman Datta and Frédérique Oggier, “In-Network Redundancy Generation for Opportunistic Speedup of Data Backup,” Future Generation Computer Systems, vol. 29, no. 6, pp. 1353–1362, 2013. Crossref, https://doi.org/10.1016/j.future.2013.02.009
[24] Dongqing Wang, Feng Ding, and Yanyun Chu, “Data Filtering Based Recursive Least Squares Algorithm for Hammerstein Systems Using the Key-Term Separation Principle,” Information Sciences, vol. 222, pp. 203–212, 2013. Crossref, https://doi.org/10.1016/j.ins.2012.07.064
[25] KaiSong et al., “Research and Application of Error Correction Theory for Ternary Optical Computer Based on Hamming Code,” Optik, vol. 267, 169647, 2022. Crossref, https://doi.org/10.1016/j.ijleo.2022.169647
[26] J. Wang, H. Wu, and R. Wang, “A New Reliability Model in Replication-Based Big Data Storage Systems,” Journal of Parallel and Distributed Computing, vol.108, pp. 14–27, 2017. Crossref, https://doi.org/10.1016/j.jpdc.2017.02.001
[27] P. Shah, and R. Oza, Information and Communication Technology for Intelligent Systems (ICTIS 2017), vol. 2, 2017. Smart Innovation, Systems and Technologies, S. Satapathy, and A. Joshi, Eds. Cham, Switzerland: Springer, vol.84, pp. 236-244, 2018.
[28] Alessandro Neri, “Twisted Linearized Reed-Solomon Codes: A Skew Polynomial Framework,” Journal of Algebra, vol. 609, pp. 792–839, 2022. Crossref, https://doi.org/10.1016/j.jalgebra.2022.06.027
[29] Han Bao, Yijie Wang, and Fangliang Xu, “Reducing Network Cost of Data Repair in Erasure-Coded Cross-Datacenter Storage,” Future Generation Computer Systems, vol. 102, pp. 494–506, 2020. Crossref, https://doi.org/10.1016/j.future.2019.08.027
[30] Victor A. Gorelov et al., “Complex Methodological Approach to Introduction of Modern Telemedicine Technologies into the Healthcare System on Federal, Regional and Municipal Levels,” 2020 International Conference Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS) pp. 468-473, 2020. Crossref, https://doi.org/10.1109/ITQMIS51053.2020.9322864
[31] AlanPinheiro et al., “Optimized Buffer Protection for Network-on-Chip Based on Error Correction Code,” Microelectronics Journal, vol. 100, p. 104799, 2020. Crossref, https://doi.org/10.1016/j.mejo.2020.104799
[32] N. I. Chervyakov et al., “Residue Number System-Based Solution for Reducing the Hardware Cost of a Convolutional Neural Network,” Neurocomputing, vol. 407, pp. 439–453, 2020.
[33] Avik Sengupta, and Balasubramaniam Natarajan, “Redundant Residue Number System Based Space-Time Block Codes,” Physical Communication, vol.12, pp. 1–15, 2014. Crossref, https://doi.org/10.1016/j.phycom.2014.01.002
[34] E. V. Kostyrin, “The Economic and Mathematical Model of Medical Organization Management,” 2020 13th International Conference Management of Large-Scale System Development-(MLSD), p. 9247652, 2020. Crossref, https://doi.org/10.1109/MLSD49919.2020.9247652
[35] Rimma Meyramovna Ualiyeva et al., “Peculiarities of the Structure of Male Reproductive System in Trematode Parastrigea Robusta (Trematoda: Strigeidae),” Online Journal of Biological Sciences, vol. 17, no. 2, pp. 88–94, 2017. Crossref, https://doi.org/10.3844/ojbsci.2017.88.94
[36] Rimma Meyramovna Ualiyeva, Sayan Berikovich Zhangazin, and Indira Bulatovna Altayeva, “Structural Organization of Vitelline Cells of Trematode with Undifferentiated Body of Azygia Lucii (Muller, 1776),” Online Journal of Biological Sciences, vol. 22, no. 1, pp. 10–17, 2022. Crossref, https://doi.org/10.3844/ojbsci.2022.10.17