Cloud-Based AI-Driven Advanced Optimization of Middleware for High-Performance Enterprise Applications
DOI:
https://doi.org/10.15662/IJEETR.2023.0505015Keywords:
cloud computing, artificial intelligence, machine learning, middleware optimization, enterprise applications, high-performance computing, cloud-native systems, intelligent resource allocation, predictive analytics, dynamic optimization, scalability, performance managementAbstract
Enterprise applications increasingly operate across distributed, heterogeneous, and cloud-based environments in which middleware serves as the critical layer connecting applications, databases, services, application programming interfaces, message brokers, and infrastructure resources. As transaction volumes, user expectations, and computational demands continue to increase, conventional middleware optimization techniques based primarily on static configuration and rule-based monitoring are often insufficient. Artificial intelligence (AI), machine learning, predictive analytics, and cloud-native computing provide opportunities to transform middleware optimization from reactive management into intelligent, adaptive, and predictive resource management. This study examines a cloud-based AI-driven approach for advanced middleware optimization with particular emphasis on application performance, scalability, resource utilization, fault tolerance, latency reduction, and operational efficiency. The proposed approach integrates real-time telemetry collection, machine-learning-based workload prediction, intelligent resource allocation, dynamic configuration, anomaly detection, and automated performance optimization. The methodology adopts a mixed experimental and analytical design in which an AI-enabled middleware optimization framework is evaluated against conventional middleware management approaches using performance indicators such as response time, throughput, CPU and memory utilization, network latency, scalability, and failure recovery time. The research aims to establish how intelligent optimization can improve the performance and reliability of enterprise applications operating in dynamic cloud environments. The study further considers challenges associated with model accuracy, data quality, security, interoperability, explainability, and computational overhead. The findings are expected to contribute to the development of adaptive middleware architectures capable of autonomously responding to changing enterprise workloads
References
1. Liang, X., et al. (2020). Intelligent task prediction and computation offloading based on mobile-edge cloud computing. Future Generation Computer Systems, 102, 925–931. https://doi.org/10.1016/j.future.2019.09.035
2. Chaba, A. (2021). API-driven enterprise commerce architecture for composable digital ecosystems. International Journal of Engineering & Extended Technologies Research, 3(6), 4082–4086.
3. Mishu, K. P., Ahmed, M. T., Mohiyan, M. S., Jim, M. M. I., Atif, M., & Chowdhury, S. A. (2022). AI-Driven Intelligent Compliance and Resilience Framework (AI-ICRF) for Critical US Aviation Spare Parts and Semiconductor Supply Chains. Well Testing Journal, 31(2), 240-271.
4. Narapareddy, V. S. R., & Yerramilli, S. K. (2022). Risk-oriented incident management in ServiceNow event management. International Journal of Engineering Technology Research & Management, 6(7), 134–149.
5. Reddy, B. P. (2021). Optimizing smart grid performance using Machine Learning: A Data-Driven Approach to Energy Efficiency. J. Electrical Systems, 17(4), 149-156.
6. Shekhar, P. C. (2022). Accelerating agile quality assurance with AI-powered testing strategies. International Journal of Scientific Research in Engineering and Management, 6(1), 1–11.
7. Rajula, A. (2022). Cloud-based virtual patient engagement with intelligent scheduling and secure document management. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9245.
8. Bellundagi, M. (2022). Performance Optimization Techniques for Enterprise Java Applications Using Middleware and Messaging Systems. International Journal of Computer Technology and Electronics Communication, 5(3), 5158-5168.
9. Vani, M., & Dadlani, D. (2023). Smart home – “Aashraya” IoT automation system. International Journal of Computer Technology and Electronics Communication, 6(1), 6393–6403.
10. Challa, R. (2022). Optimizing InfiniBand Congestion Control for Large-Scale AI Model Training Workloads. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5749-5757.
11. Tasleem, N. (2017). Service catalog optimization in HR. International Journal of Applied Research, 3, 1090-1097.
12. Rahul Reddy Bandhela, RamMohan Reddy Kundavaram. (2023). A Comparative Study on Neural Network Architectures for Image Recognition Applications . Journal of Computational Analysis and Applications (JoCAAA), 31(1), 1334–1342. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3566
13. Koganti, H. (2021). Machine learning-driven performance anomaly detection and auto-tuning in distributed Java full-stack systems: A comprehensive review. International Journal of Research Publications in Engineering, Technology and Management, 4(3), 4977–4986.
14. Yepuri, V. K., Polamarasetty, V. K., Donthi, S., & Gondi, A. K. R. (2023). Containerization of a polyglot microservice application using Docker and Kubernetes.arXiv preprint arXiv:2305.00600
15. Chandra, G., Redd, P., & Kadali, R. S. (2022). Lightweight Linux–Powered Edge Systems: Facilitating Secure and Efficient Container Workloads at the Edge. J. Electrical Systems, 18(4), 151-161.
16. Gujarathi, M. (2022). Rule externalization for enterprise validation platforms: Architecture and design considerations. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7163-7167.
17. Chy, M. S. K., Abdullah, S. M., Nabil, M. A., Alam, A., Himeluzzaman, M., Onik, T. A., ... & Khan, H. A. (2022). QShield-NS: A Variational Quantum Machine Learning Model for Zero-Day Cyber Threat Detection in National Security Systems. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9283.
18. Padmanabham, S. (2022). Secure enterprise architecture for pharmacy benefit management platforms. International Journal of Engineering & Extended Technologies Research, 4(5), 5381–5386.
19. Vemireddy, S. (2022). Modernizing enterprise financial platforms through distributed cloud architectures. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7420–7426.
20. Bandaru, P. K. (2021). Leveraging hardware-in-the-loop simulation for continuous automotive software testing. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3730–3735.
21. 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.
22. Raj, A. A., & Sugumar, R. (2022, October). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) (pp. 1-6). IEEE.
23. Raja, G. V. (2022). AI-Driven Enterprise Architecture Integrating Zero-Trust Security for Secure Scalable and Compliant Cloud-Native Platforms. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12178.
24. Gopinathan, V. R., & Mali, R. K. (2022). Building cognitive technology frameworks through artificial intelligence SAP cloud automation and enterprise intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7152-7162.
25. Soundappan, S. J. (2022). Integrated Risk Governance Framework for Financial Compliance Supply Chain Resilience and Enterprise Data Management. International Journal of Computer Technology and Electronics Communication, 5(6), 16254-16263.
26. Anand, L. (2022). Integrating Kubernetes Microservices with Privileged Access Security and Real-Time Fraud Detection for Modern Enterprise Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7453-7461.
27. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
28. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
29. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
30. Sugumar, R. (2023, September). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.
31. Hoque, M. J., Akter, F., & Mohammad, A. R. (2019). Data-Driven Decision Support System for Restaurant Business Optimization. American Journal of Economics and Business Management, 2(2).
