Scalable AI Powered Data Processing Architectures for High Performance Distributed Systems
DOI:
https://doi.org/10.15662/IJEETR.2025.0706044Keywords:
Artificial Intelligence, Distributed Systems, High Performance Computing, Machine Learning, Big Data Analytics, Cloud Computing, Edge Computing, Parallel Processing, Intelligent Automation, Resource Optimization, Predictive Analytics, Distributed Databases, Scalable Systems, Real-Time ProcessingAbstract
Scalable AI-powered data processing architectures are transforming high-performance distributed systems by enabling intelligent, adaptive, and efficient handling of massive volumes of data generated in modern digital environments. The rapid growth of cloud computing, Internet of Things (IoT), big data analytics, edge computing, and enterprise applications has created significant challenges in processing, storing, and analyzing data at scale. Traditional distributed processing systems often struggle with issues related to latency, resource allocation, scalability, fault tolerance, and real-time analytics. Artificial Intelligence (AI) technologies such as machine learning, deep learning, predictive analytics, and intelligent automation provide advanced capabilities for optimizing data processing operations in distributed infrastructures.
This study explores scalable AI-powered data processing architectures designed for high-performance distributed systems. The research focuses on intelligent workload management, distributed data analytics, automated resource optimization, predictive performance monitoring, and fault-tolerant processing mechanisms. It also examines the integration of AI with cloud-native platforms, distributed databases, parallel computing frameworks, and edge computing infrastructures. Furthermore, the study investigates implementation challenges including computational complexity, interoperability issues, data privacy concerns, and infrastructure costs. The findings demonstrate that AI-powered data processing architectures significantly improve scalability, operational efficiency, throughput, reliability, and real-time decision-making capabilities. These architectures are expected to play a crucial role in future intelligent computing ecosystems and next-generation distributed digital infrastructures.
References
1. Pothuri, M. K. Building a Seamless Healthcare Data Fabric: Zero-Touch Integration and Scalable Mapping Across Provider, Claims, Recipient, and Pharmacy Source Systems for State Medicaid. IJLRP-International Journal of Leading Research Publication, 6(8).
2. Panyala, V. R. (2024). Designing self-healing cloud architectures for mission-critical distributed systems. International Journal of Science, Research and Technology, 7(2), 11717–11721.
3. Shewale, V. (2025). Demystifying the MITRE ATT&CK Framework: A Practical Guide to Threat Modeling. Journal of Computer Science and Technology Studies, 7(3), 182-186.
4. Rongali, L. P. (2025). Compliance and Governance: Address the Role of Devops in Maintaining Compliance and Ensuring Governance throughout the Development Lifecycle. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5229546
5. Bheemisetty, N. (2024). AI-Powered Recommendation Systems Best Practices and Real-World Applications. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13926.
6. Kassetty, N., Alang, K., Paruchuru, V., Sharma, S., Goel, P., & Kumar, S. (2025, May). Cloud Security Management: Advanced AI Techniques for Anomaly Detection and Response Automation. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 1620-1624). IEEE.
7. Pasumarthi, H. (2023). A Deep Dive into Enterprise B2B Integrations: Designing High-Availability File and API Workflows with IBM Datapower and Autosys. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(2), 8363-8370.
8. Mulla, F. A. (2024). Modern Mobile Testing Tools: A Comprehensive Guide to Quality Assurance and Automation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 10-32628.
9. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
10. Macha, Y., & Pulichikkunnu, S. K. (2023). An Explainable AI System for Fraud Identification in Insurance Claims via Machine-Learning Methods. Int. J. Adv. Res. Sci. Commun. Technol, 3(3), 1391-1400.
11. Bellundagi, M. (2023). Design of an Intelligent Clinical Decision Support System Using Machine Learning Techniques. International Journal of Research and Applied Innovations, 6(6), 10075-10081.
12. Adepu, G. (2024). AI-driven healthcare payment systems using intelligent claims validation and fraud detection mechanisms. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 259–277.
13. Adepu, R. (2021). Modernizing legacy data centers through virtualization and software-defined infrastructure. International Journal of Research and Applied Innovations (IJRAI), 4(4), 17–36.
14. Mallireddy, S. (2024). Transforming financial services business through servicenow. International Journal of Computer Technology and Electronics Communication, 7(3), 1-6.
15. Ambalakannu, M. (2025). Accelerating Claims Processing with Observability and Automated Dashboards. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12179-12186.
16. Sarabu, V. B. (2022). Hybrid on-premise to cloud data migration: A controlled one-way synchronization framework for enterprise-scale modernization. International Journal of Science, Research and Technology (IJSRAT), 5(5), 19–33.
17. Hossain, M. S., Hossain, M. S., Ali, M., & Rahman, M. W. (2025). Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States. Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States, 1(7), 121-146.
18. Nijaguna, G.S.; Manjunath, D.R.; Abouhawwash, M.; Askar, S.S.; Basha, D.K.; Sengupta, J. Deep Learning-Based Improved WCM Technique for Soil Moisture Retrieval with Satellite Images. Remote Sens. 2023, 15, 2005.
19. Vayyasi, N. K. (2023). Designing a multi-domain predictive framework using Java and generative AI for financial, retail, and industrial use cases. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(6), 8060–8069.
20. Anbazhagan, K. (2025). AI Driven Zero Trust Security Model for Enterprise Data Protection and Intelligent Infrastructure Management. International Journal of Technology, Management and Humanities, 11(03), 101-107.
21. Appani, C. (2024). Explainable AI for fraud detection in financial transactions. Journal of Information Systems Engineering and Management, 9(3). https://jisem-journal.com/download/32_Explainable_AI_for_Fraud_Detection.pdf
22. Archana, R., & Anand, L. (2025). Residual u-net with Self-Attention based deep convolutional adaptive capsule network for liver cancer segmentation and classification. Biomedical Signal Processing and Control, 105, 107665.
23. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
24. Gopinathan, V. R. (2024). Real-Time Financial Risk Intelligence Using Secure-by-Design AI in SAP-Enabled Cloud Digital Banking. International Journal of Computer Technology and Electronics Communication, 7(6), 9837-9845.
25. Praveena, M., Saravanan, M., & Yerra, R. (2025, June). PSO MPPT based Control Framework for Photovoltaic Systems to enhance Power Quality. In 2025 5th International Conference on Intelligent Technologies (CONIT) (pp. 1-5). IEEE.
26. Murugeshwari, B., Sabatini, S. A., Jose, L., & Padmapriya, S. (2023). Effective data aggregation in WSN for enhanced security and data privacy. arXiv preprint arXiv:2304.14654.
27. Anbazhagan, K. (2024). Trustworthy and Adaptive AI Systems for Enterprise Analytics Cybersecurity and Decision Optimization Using API-First and Cloud-Native Architectures. International Journal of Technology, Management and Humanities, 10(03), 65-74.
28. Raja, G. V. (2023). Modernizing Enterprise Systems using AI with Machine Learning and Cloud Computing for Intelligent Systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11713.
29. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
30. Vimal, V. R., Jayalakshmi, D., Narayanan, L. K., Hemavathi, R., & Loganayagi, S. (2024, November). 5G-Enabled Remote Healthcare Monitoring for Improved Patient Care. In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET) (pp. 1-5). IEEE.
31. Udayakumar, S. Y. P. D. (2023). User Activity Analysis Via Network Traffic Using DNN and Optimized Federated Learning based Privacy Preserving Method in Mobile Wireless Networks.
32. Mathew, A. (2024). Cloud data sovereignty governance and risk implications of cross-border cloud storage. Information Systems Audit and Control Association.
33. Mulajkar, R. M., & Gohokar, V. V. (2017, February). Development of Semi-Automatic Methodology for Extraction of Depth for 2D-to-3D Conversion. In Proceedings of the 9th International Conference on Machine Learning and Computing (pp. 373-378).
34. Reddy, B. V. S., & Sugumar, R. (2025, April). Improving dice-coefficient during COVID 19 lesion extraction in lung CT slice with watershed segmentation compared to active contour. In AIP Conference Proceedings (Vol. 3270, No. 1, p. 020094). AIP Publishing LLC.
35. Prasad, P. K. (2024). Establishing AI governance frameworks within CloudOps to accelerate safe, compliant AI adoption at scale. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 14026–14030.
36. Rao, G. R. (2023). Hidden Trade-Offs in Modern Frontend Architecture. International Journal of Computer Technology and Electronics Communication, 6(5), 7615-7625.
37. Ganesan M. (2025). Artificial intelligence AI driven proactive customer service excellence platform in e commerce industry. International Journal of Computer Technology and Electronics Communication 8(1) 10089–10099.
