|
Аннотация.
Целью данного исследования является разработка расширяемой микросервисной архитектуры программного комплекса анализа данных, способной к горизонтальному масштабированию и интеграции пользовательских алгоритмов в режиме реального времени. Предлагаемое решение основано на совмещении микроядерной и микросервисной архитектур и реализовано на примере программного комплекса анализа гиперспектральных изображений. Это решение демонстрирует высокую эффективность и простоту добавления новых алгоритмов с использованием современных подходов к контейнеризации программного кода. Реализованный программный комплекс демонстрирует масштабируемость и расширяемость предложенной архитектуры.
Ключевые слова:
анализ данных, микросервисная архитектура, микроядерная архитектура, функциональное масштабирование, горизонтальное масштабирование, интеграция алгоритмов.
DOI 10.14357/20718632250307
EDN SQPKMV
Стр. 73-85.
Литература
1. Sharma K., Giannakos M. Multimodal data capabilities for learning: What can multimodal data tell us about learning? // British Journal of Educational Technology. 2020. Vol. 51, № 5. P. 1450–1484. 2. Liu X., Iftikhar N., Xie X. Survey of real-time processing systems for big data // Proceedings of the 18th International Database Engineering & Applications Symposium on - IDEAS ’14. 2014. 3. Rawat R., Yadav R. Big Data: Big Data Analysis, Issues and Challenges and Technologies // IOP Conference Series: Materials Science and Engineering. 2021. Vol. 1022, № 1. P. 012014. 4. Angel N.A. et al. Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies // Sensors. 2021. Vol. 22, № 1. P. 196. 5. Deng Z. et al. Deep learning in food authenticity: Recent advances and future trends // Trends in Food Science and Technology. Elsevier BV, 2024. Vol. 144. P. 104344–104344. 6. Takahashi S. et al. Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review // Journal of Medical Systems. Springer Science+Business Media, 2024. Vol. 48, № 1. 7. Patil R., Venkat Gudivada. A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs) // Applied sciences (Basel). Multidisciplinary Digital Publishing Institute, 2024. Vol. 14, № 5. P. 2074–2074. 8. Blinowski G., Ojdowska A., Przybyłek A. Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation // IEEE Access. 2022. Vol. 10, № 2169-3536. P. 20357–20374. 9. Haorongbam L., Nagpal R., Sehgal R. Service Oriented Architecture (SOA): A Literature Review on the Maintainability, Approaches and Design Process // 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence). 2022. 10. Manchana R. Event-Driven Architecture: Building Responsive and Scalable Systems for Modern Industries // International Journal of Science and Research (IJSR). 2021. Vol. 10, № 1. P. 1706–1716. 11. Akaash Vishal Hazarika, Shah M. Serverless Architectures: Implications for Distributed System Design and Implementation // International Journal of Science and Research (IJSR). 2024. Vol. 13, № 12. P. 1250–1253. 12. Sohail S. Plug-In based Software Architecture for the Development of Sustainable Software Ecosystem: Do’s and Don’ts [Electronic resource]. OSF Preprints, 2024. URL: osf.io/ftuzw_v1. 13. Poniszewska-Marańda A., Czechowska E. Kubernetes Cluster for Automating Software Production Environment // Sensors. 2021. Vol. 21, № 5. P. 1910. 14. Carrión C. Kubernetes as a Standard Container Orchestrator - A Bibliometric Analysis // Journal of Grid Computing. 2022. Vol. 20, № 4. 15. Sifat Ibtisum et al. A comparative analysis of big data processing paradigms: Mapreduce vs. apache spark // World Journal Of Advanced Research and Reviews. GSC Online Press, 2023. Vol. 20, № 1. P. 1089–1098. 16. Moritz P. et al. Ray: A Distributed Framework for Emerging AI Applications // arXiv (Cornell University). Cornell University, 2017. 17. Boettiger C. An introduction to Docker for reproducible research // ACM SIGOPS Operating Systems Review. 2015. Vol. 49, № 1. P. 71–79. 18. Beetz F., Harrer S. GitOps: The Evolution of DevOps? // IEEE Software. 2022. Vol. 39, № 4. P. 70–75. 19. Birk R.J., McCord T.B. Airborne hyperspectral sensor systems // IEEE Aerospace and Electronic Systems Magazine. 1994. Vol. 9, № 10. P. 26–33. 20. Saari H. et al. Novel miniaturized hyperspectral sensor for UAV and space applications // Proceedings of SPIE. SPIE, 2009. Vol. 7474. 21. Yu H. et al. A critical review on applications of hyperspectral remote sensing in crop monitoring // Experimental Agriculture. Cambridge University Press, 2022. Vol. 58. 22. Cheshkova A.F. A review of hyperspectral image analysis techniques for plant disease detection and identif ication // Vavilov Journal of Genetics and Breeding. 2022. Vol. 26, № 2. P. 202–213. 23. Zhang F., Zhou G. Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize // BMC Ecology. 2019. Vol. 19, № 1. 24. NIE J. et al. Advances in hyperspectral remote sensing for precision fertilization decision-making: a comprehensive overview // TURKISH JOURNAL OF AGRICULTURE AND FORESTRY. Scientific and Technological Research Council of Turkey (TUBITAK), 2024. Vol. 48, № 6. P. 1084–1104. 25. Schratz P. et al. Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques? // Remote Sensing. 2021. Vol. 13, № 23. P. 4832. 26. Thangavel K. et al. Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire // Remote Sensing. 2023. Vol. 15, № 3. P. 720. 27. Ye C. et al. Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains // IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019. Vol. 12, № 12. P. 5047–5060. 28. Huang H.-Y. et al. A Review of Recent Advances in Computer-Aided Detection Methods Using Hyperspectral Imaging Engineering to Detect Skin Cancer // Cancers. 2023. Vol. 15, № 23. P. 5634. 29. Chiang N. et al. Evaluation of hyperspectral imaging technology in patients with peripheral vascular disease // Journal of Vascular Surgery. Elsevier BV, 2017. Vol. 66, № 4. P. 1192–1201. 30. Vázquez-Ingelmo A., García-Holgado A., García-Peñalvo F.J. C4 model in a Software Engineering subject to ease the comprehension of UML and the software [Electronic resource] // IEEE Xplore. 2020. P. 919–924. URL: https://ieeexplore.ieee.org/abstract/document/9125335. 31. Vinoski S. Advanced Message Queuing Protocol // IEEE Internet Computing. 2006. Vol. 10, № 6. P. 87–89. 32. Zhang D. et al. Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging // International Journal of Agriculture and Biology. 2016. Vol. 18, № 04. P. 747–756. 33. Gao B. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space // Remote Sensing of Environment. 1996. Vol. 58, № 3. P. 257–266. 34. Huete A.R. A soil-adjusted vegetation index (SAVI) // Remote Sensing of Environment. 1988. Vol. 25, № 3. P. 295–309. 35. Paringer R, Mukhin A., Kupriyanov A. Formation of an informative index for recognizing specified objects in hyperspectral data // Computer Optics. 2021. Vol. 45, № 6. 36. Barreda-Ángeles M. et al. Unconscious Physiological Effects of Search Latency on Users and Their Click Behaviour // 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2015. 37. Arapakis I., Bai X., Cambazoglu B.B. Impact of response latency on user behavior in web search // Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval - SIGIR ’14. 2014.
|
