Forming the Architecture of a Multi-Layered Model of Physical Data Storage for Complex Telemedicine Systems

Dmitry V. Polezhaev, Aslan A. Tatarkanov, Islam A. Alexandrov


The relevance of this research is determined by the need to study the issues of improving data storage technologies for complex telemedicine systems. The objective is to create a multi-layered data storage model for complex telemedicine systems to ensure the most complete use of their capacity and the timely expansion of existing storage. The research is conducted on the basis of an analysis of existing opportunities and problems in the field of data storage technologies. An analysis of the main features of the development of data storage technologies revealed that the existing models have no detailed description of the recording and physical storage of data bits, which is necessary for describing the storage process. Different architectures are reviewed, and their strengths and weaknesses are discussed. Within the framework of a demonstration experiment using the Kohonen neural network apparatus as a tool for solving the problem of placing objects in accordance with the required parameters, it is shown that the proposed storage system resource management model is operable and allows solving the problem of rational use of physical resources. As a result, a multilevel model of data storage is proposed, which combines the levels of storage process organization and technology. The distinguishing feature of this method is the comparison of storage organization levels, data media, and characteristics of physical storage and stored files.


Doi: 10.28991/HIJ-2023-04-04-09

Full Text: PDF


Telemedicine System; Data Storage System; Direct Attached Storage (DAS); Network Attached Storage (NAS); Storage Area Network (SAN); Fabric Attached Storage (FAS); Network Direct Attached Storage (NDAS); Virtualization Technology; Stratified Structure.


Kruk, M. E., Gage, A. D., Arsenault, C., Jordan, K., Leslie, H. H., Roder-DeWan, S., Adeyi, O., Barker, P., Daelmans, B., Doubova, S. V., English, M., García-Elorrio, E., Guanais, F., Gureje, O., Hirschhorn, L. R., Jiang, L., Kelley, E., Lemango, E. T., Liljestrand, J., . . . Pate, M. (2018). High-quality health systems in the Sustainable Development Goals era: time for a revolution. The Lancet Global Health, 6(11), e1196–e1252. doi:10.1016/s2214-109x(18)30386-3.

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 54. doi:10.1186/s40537-019-0217-0.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. New England Journal of Medicine, 375(13), 1216–1219. doi:10.1056/nejmp1606181.

Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017). Disease prediction by machine learning over big data from healthcare communities. IEEE Access, 5, 8869–8879. doi:10.1109/access.2017.2694446.

Lebedev, G. S., Linskaya, E. Y., Tatarkanov, A. A., & Lampezhev, A. K. (2023). Recent solutions in the field of automated monitoring and quality control of telemedical services. International Journal of Engineering Trends and Technology, 71(1), 62–78. doi:10.14445/22315381/ijett-v71i1p207.

Kuklin, V. Z., Alexandrov, I. A., Umyskov, A. A., & Lampezhev, A. K. (2022). Analysis of the prospects for developing storage and processing complexes for multiformat media data. Journal of Computer Science, 18(12), 1159–1169. doi:10.3844/jcssp.2022.1159.1169.

Chen, X., Yan, C. C., Zhang, X., Zhang, X., Dai, F., Yin, J., & Zhang, Y. (2015). Drug–target interaction prediction: databases, web servers and computational models. Briefings in Bioinformatics, 17(4), 696–712. doi:10.1093/bib/bbv066.

Li, J., Zheng, S., Chen, B., Butte, A. J., Swamidass, S. J., & Lu, Z. (2015). A survey of current trends in computational drug repositioning. Briefings in Bioinformatics, 17(1), 2–12. doi:10.1093/bib/bbv020.

Hale, T. M., & Kvedar, J. C. (2014). Privacy and security concerns in telehealth. AMA Journal of Ethics, 16(12), 981–985. doi:10.1001/virtualmentor.2014.16.12.jdsc1-1412.

Ahmad, R. W., Salah, K., Jayaraman, R., Yaqoob, I., Ellahham, S., & Omar, M. (2021). The role of blockchain technology in telehealth and telemedicine. International Journal of Medical Informatics, 148, 104399. doi:10.1016/j.ijmedinf.2021.104399.

Tatarkanov, A., Lampezhev, A., Polezhaev, D., & Tekeev, R. (2022). Suboptimal biomedical diagnostics in the presence of random perturbations in the data. International Journal of Engineering Trends and Technology, 70(11), 129–137. doi:10.14445/22315381/ijett-v70i11p213.

Rashid, A., Salamat, N., & Prasath, V. (2018). An algorithm for data hiding in radiographic images and ePHI/R application. Technologies, 6(1), 7. doi:10.3390/technologies6010007.

Tatarkanov, A. A., Umyskov, L., Tekeev, R. K., & Kuklin, V. Z. (2022). Model development of universal hardware and software module for medical information system. International Journal of Emerging Technology and Advanced Engineering, 12(10), 136–146. doi:10.46338/ijetae1022_15.

Xia, Q., Sifah, E. B., Asamoah, K. O., Gao, J., Du, X., & Guizani, M. (2017). MeDShare: trust-less medical data sharing among cloud service providers via blockchain. IEEE Access, 5, 14757–14767. doi:10.1109/access.2017.2730843.

Alyass, A., Turcotte, M., & Meyre, D. (2015). From big data analysis to personalized medicine for all: challenges and opportunities. BMC Medical Genomics, 8(1), 1-12. doi:10.1186/s12920-015-0108-y.

Dimitrov, D. V. (2016). Medical Internet of Things and Big Data in Healthcare. Healthcare Informatics Research, 22(3), 156-163. doi:10.4258/hir.2016.22.3.156.

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “Big Data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115. doi:10.1016/

Warren, J., & Marz, N. (2015). Big Data: Principles and Best Practices of Scalable Realtime Data Systems. Manning Publications: Shelter Island, New York, USA.

Haghi, M., Thurow, K., & Stoll, R. (2017). Wearable devices in medical internet of things: scientific research and commercially available devices. Healthcare Informatics Research, 23(1), 4. doi:10.4258/hir.2017.23.1.4.

Jennings, B., & Stadler, R. (2014). Resource management in clouds: survey and research challenges. Journal of Network and Systems Management, 23(3), 567–619. doi:10.1007/s10922-014-9307-7.

Hong, C. H., & Varghese, B. (2019). Resource Management in Fog/Edge Computing. ACM Computing Surveys, 52(5), 1–37. doi:10.1145/3326066.

Musaddiq, A., Zikria, Y. B., Hahm, O., Yu, H., Bashir, A. K., & Kim, S. W. (2018). A survey on resource management in IoT operating systems. IEEE Access, 6, 8459–8482. doi:10.1109/access.2018.2808324.

Yoshida, H. (2018). Storage resource management. Encyclopedia of Database Systems, 3759–3760. doi:10.1007/978-1-4614-8265-9_1342.

Grozev, N., & Buyya, R. (2013). Performance modelling and simulation of three-tier applications in cloud and multi-cloud environments. The Computer Journal, 58(1), 1–22. doi:10.1093/comjnl/bxt107.

Fu, J. S., Liu, Y., Chao, H. C., Bhargava, B. K., & Zhang, Z. J. (2018). Secure Data Storage and Searching for Industrial IoT by Integrating Fog Computing and Cloud Computing. IEEE Transactions on Industrial Informatics, 14(10), 4519–4528. doi:10.1109/tii.2018.2793350.

Gierek, M., Kitala, D., Łabuś, W., Glik, J., Szyluk, K., Pietrauszka, K., Bergler-Czop, B., & Niemiec, P. (2023). The impact of telemedicine on patients with Hidradenitis Suppurativa in the COVID-19 Era. Healthcare, 11(10), 1453. doi:10.3390/healthcare11101453.

Calton, B. A., Nouri, S., Davila, C., Kotwal, A., Zapata, C., & Bischoff, K. E. (2023). Strategies to make telemedicine a friend, not a foe, in the provision of accessible and equitable cancer care. Cancers, 15(21), 5121. doi:10.3390/cancers15215121.

Troschke, T., Wieczorek, A., Kulinski, K., Ociepa, T., Zielezinska, K., Lode, H. N., & Urasinski, T. (2023). Pediatric hematology and oncology center integrated by telemedicine: experience, challenges and first results of a cross border network. Healthcare, 11(10), 1431. doi:10.3390/healthcare11101431.

Kumar, K. (2020). From post-industrial to post-modern society. In The Information Society Reader, Routledge, pp. 103-120.

Akhtar, D. N., Kerim, D. B., Perwej, D. Y., Tiwari, D. A., & Praveen, D. S. (2021). A comprehensive overview of privacy and data security for cloud storage. International Journal of Scientific Research in Science, Engineering and Technology, 113–152. doi:10.32628/ijsrset21852.

Chen, Y., Ding, S., Xu, Z., Zheng, H., & Yang, S. (2018). Blockchain-based medical records secure storage and medical service framework. Journal of Medical Systems, 43(1). doi:10.1007/s10916-018-1121-4.

Yang, P., Xiong, N., & Ren, J. (2020). Data security and privacy protection for cloud storage: a survey. IEEE Access, 8, 131723–131740. doi:10.1109/access.2020.3009876.

Cha, B., Park, S., Kim, J., Pan, S., & Shin, J. (2018). International network performance and security testing based on distributed abyss storage cluster and draft of data lake framework. Security and Communication Networks, 2018, 1–14. doi:10.1155/2018/1746809.

Malav, V., & Sharma, D. A. (2018). Effect and benefits of deploying Hadoop in private cloud. National Journal of Multidisciplinary Research and Development, 3, 1057-1062.

Park, J. K., & Kim, J. (2018). Big data storage configuration and performance evaluation utilizing NDAS storage systems. AKCE International Journal of Graphs and Combinatorics, 15(2), 197–201. doi:10.1016/j.akcej.2017.09.003.

Edelson, E. (2004). Security in Network Attached Storage (NAS) for Workgroups. Network Security, 2004(4), 8–12. doi:10.1016/s1353-4858(04)00065-0.

Salim, N. B., Zambri, N. A., Suhaimi, M. B., & Sim, S. Y. (2023). Automatic generation control system: the impact of battery energy storage in multi area network. International Journal of Integrated Engineering, 15, 208–216. doi:10.30880/ijie.2023.15.03.022.

Liu, M. (2023). Fabric-centric computing. In Proceedings of the 19th Workshop on Hot Topics in Operating Systems (pp. 118–126), Association for Computing Machinery, Providence, RI, USA. doi:10.1145/3593856.3595907.

Okafor, I., Ramanathan, A. K., Challapalle, N. R., Li, Z., & Narayanan, V. (2023). Fusing in-storage and near-storage acceleration of convolutional neural networks. ACM Journal on Emerging Technologies in Computing Systems, 20(1), 1–22. doi:10.1145/3597496.

Zet, C., Dumitriu, G., Fosalau, C., & Sarbu, G. C. (2023). Automated calibration and DCC generation system with storage in private permissioned Blockchain network. Acta IMEKO, 12(1), 1–7. doi:10.21014/actaimeko.v12i1.1414.

Full Text: PDF

DOI: 10.28991/HIJ-2023-04-04-09


  • There are currently no refbacks.

Copyright (c) 2023 Dmitry V. Polezhaev, Aslan A. Tatarkanov, Islam A. Alexandrov