The role of distributed computing systems in modern technological ecosystems
https://doi.org/10.21869/2223-1536-2025-15-3-40-49
Abstract
The purpose of research is to analyze the role and significance of distributed computing systems (DCS) in shaping and developing key areas of modern digital infrastructure, as well as to identify the prospects and challenges related to their further integration into technological ecosystems.
Methods. The materials used include statistical data and analytical reports from authoritative sources (Statista, DBEngines, MarketsandMarkets, etc.), as well as technical specifications of frameworks and systems (Apache Hadoop, Cassandra, IBM Summit , etc.). The methodology involves comparative analysis, generalization of practical case studies, and forecasting based on technological development trends.
Results. It has been established that DCS form the foundation of cloud computing, Big Data, IoT, high-performance computing (HPC), and blockchain technologies. Key technological trends have been identified: integration with artificial intelligence, the growth of edge and fog computing, the development of quantum distributed architectures, and trusted computing. Noted risks include management complexity, cybersecurity vulnerabilities, scalability challenges, and legal issues.
Conclusion. Distributed computing systems play a crucial role in digital transformation. Their implementation ensures fault tolerance, scalability, and high performance of IT services. The future of DCS lies in AI integration, automated management, and adaptation to emerging computing models. Sustainable development of DCS requires advances in security, regulatory frameworks, and optimization of network infrastructure.
About the Authors
S. A. NesterovichRussian Federation
Sergey A. Nesterovich, Candidate of Sciences (Engineering), Associate Professor, Associate Professor of the Department of Information Technology, Artificial Intelligence and Socio-Social Technologies of the Digital Society
4/1 Wilhelm Peak Str., Moscow 129226
A. N. Brezhneva
Russian Federation
Aleksandra N. Brezhneva, Candidate of Sciences (Engineering), Associate Professor of the Department of Informatics
36 Stremyanny side-street, Moscow 117997
References
1. Khan M.S., Lee H.J. Edge computing in IoT: Architecture and security. J. of Network and Computer Applications. 2024;226:103884.
2. Al-Shareeda M.A., Hassan R., Ismail M. Review of edge computing for the Internet of Things. J. of Sensor Network & Data Communications. 2024;4(1):1–11.
3. Connected devices in the IoT 2019–2030. 2024. Statista. Available at: https://www.statista.com/statistics/1101442 (accessed 08.06.2025).
4. Distributed architecture design. Microsoft Learn, 2023. Microsoft Azure. Available at: https://learn.microsoft.com/en-us/azure/architecture (accessed 08.06.2025).
5. Distributed systems on Google Cloud. Google Cloud Docs, 2023. Google Cloud. Available at: https://cloud.google.com/architecture (accessed 08.06.2025).
6. Global cloud services market 2023–2027. 2024. Statista. Available at: https://www.statista.com/statistics/1060118 (accessed 08.06.2025).
7. Netflix tech stack and distributed analytics. TechCrunch Reports, 2023. TechCrunch. Available at: https://techcrunch.com/netflix-distributed (accessed 08.06.2025).
8. Apache Cassandra™ Performance. 2023. DataStax. Available at: https://www.datastax.com/ resources/benchmark (accessed 08.06.2025).
9. Distributed database architecture. MongoDB Docs, 2023. MongoDB. Available at: https://www.mongodb.com/docs (accessed 08.06.2025).
10. Ranking of Database Management Systems. 2024. DB-Engines. URL: https://db-engines.com/en/ranking (accessed 08.06.2025).
11. High Performance Computing Market – Forecast to 2028. 2023. MarketsandMarkets. Available at: https://www.marketsandmarkets.com (accessed 08.06.2025).
12. СBig Data Market Analysis 2023–2030. 2024. MarketsandMarkets. Available at: https://www.marketsandmarkets.com (accessed 08.06.2025).
13. Cambridge Centre for Alternative Finance. 4th Global Cryptoasset Benchmarking Study. Cambridge University, 2023. Available at: https://ccaf.io/publications (accessed 08.06.2025).
14. Jiao L., Zhao J., Liu Z. Federated Learning meets Edge Computing: Opportunities and Challenges. IEEE Internet of Things Journal. 2023;10(2):903–918.
15. Velasquez C.J., Rehman A. Trends in Fog Computing: Recent advances. IEEE Access. 2024;12:33212–33225.
16. Zhang Y., Wang L. Quantum computing in distributed systems. Nature Reviews Computer Science. 2023;1(4):78–90.
17. Confidential computing: technologies and use cases. Intel White Paper, 2023. Intel. Available at: https://www.intel.com/confidential-computing (accessed 08.06.2025).
18. Trends in Distributed AI and Federated Learning. OpenAI Research Blog, 2023. OpenAI. Available at: https://openai.com/research (accessed 08.06.2025).
19. White D. Designing scalable distributed systems. ACM Queue. 2023;21(3):55–70.
20. Harish V., Sridevi R. Performance analysis of public and private blockchains. In: Proc. of ICNSBT 2023. Vol. 735. Singapore: Springer; 2024. P. 233–244.
21. IBM Summit Supercomputer Overview. IBM Research, 2023. IBM. Available at: https://www.research.ibm.com/summit (accessed 08.06.2025).
22. Worldwide Edge Spending Guide. 2024. IDC. Available at: https://www.idc.com (accessed 08.06.2025).
23. Otte P., de Vos M. Distributed systems: from blockchain to IoT. Future Generation Computer Systems. 2023;141:1–15.
24. Distributed computing architectures in modern IT. Red Hat White Paper, 2023. Red Hat. Available at: https://www.redhat.com/en/resources (accessed 08.06.2025).
25. Syed A.A., Gani A., Buyya R.A review on distributed systems for big data. Information Systems Frontiers. 2023:25;99–121.
Review
For citations:
Nesterovich S.A., Brezhneva A.N. The role of distributed computing systems in modern technological ecosystems. Proceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering. 2025;15(3):40-49. (In Russ.) https://doi.org/10.21869/2223-1536-2025-15-3-40-49


