Articles Open Access
Architecting Enterprise Software Platforms with DevOps Automation and Cloud Infrastructure Technologies for Operational Excellence
Software Architect, Vélizy-Villacoublay, France
Abstract
Enterprise software platforms have become the foundation of digital transformation by enabling organizations to deliver scalable, secure, and highly available business services across cloud environments. However, conventional software architectures often rely on manual deployment processes, fragmented operational workflows, and isolated infrastructure management practices, resulting in delayed software releases, configuration inconsistencies, operational inefficiencies, and increased security risks. DevOps automation integrated with cloud infrastructure technologies provides a comprehensive solution for accelerating software delivery while improving infrastructure reliability, governance, and operational excellence. This research proposes an enterprise software platform architecture that combines DevOps automation, cloud-native infrastructure, Infrastructure as Code (IaC), container orchestration, continuous integration and continuous deployment (CI/CD), DevSecOps, automated quality assurance, and intelligent monitoring to establish scalable and resilient enterprise systems. The proposed framework automates infrastructure provisioning, application deployment, configuration management, security validation, compliance monitoring, and operational analytics throughout the software lifecycle. Continuous monitoring, predictive analytics, and AI-assisted operational intelligence further enhance infrastructure resilience, workload optimization, and incident response. The architecture significantly improves deployment consistency, software quality, business agility, and cloud resource utilization while minimizing operational risks and manual intervention. Ultimately, the proposed framework enables organizations to achieve operational excellence through secure, automated, cloud-native enterprise software platforms capable of supporting continuous innovation, digital transformation, and sustainable business growth.
Keywords
DOI
PDF Download
Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 8 |
| Number | 4 |
| Pages | 14973-14982 |
| Published | 2024-07-08 |
| Issue | Vol. 8 No. 4 (2024): International Research Journal of Innovative Engineering (IRJIE) |
References
1. Pokala, H. K. (2023). Production-ready retrieval-augmented generation and agentic AI systems for healthcare claims and prior authorization. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 993-1002.
2. 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.
3. Rella, B. P. (2021). Real-time data processing for machine learning: Streaming architectures, challenges, and use cases. IRE Journals, 5(4), 230–236.
4. Meesala, A. (2023). A distributed Kafka-centric framework for high-throughput mid-price computation and intelligent time-series persistence in financial clouds. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN, 2456-3307.
5. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
6. Mohammed, S. (2023). Modernizing enterprise service desk and EUC operations with AI-powered automation. International Journal of Computer Technology and Electronics Communication, 6(6), 8133-8136.
7. Mathew, A., & Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science, Vol. 4, 154-164.
8. Juvvadi, R. R. (2022). Machine learning for anomaly detection in the financial close: A journal entry risk-scoring framework for SAP S/4HANA. International Journal of Communication Networks and Information Security, 14(3), 1684–1695.
9. Potdar, A. (2023). Designing secure sovereign cloud architectures for enterprise data analytics and digital transformation. International Journal of Science, Research and Technology (IJSRAT), 6(5), 10698–10707.
10. Hossain, M. B., Rahman, R., & Hoque, K. (2021). Feature-Driven Supervised Learning for Detecting DDoS Attack. International Journal of Science and Research Archive, 4(01), 393-402.
11. Alex Roney Mathew. (2019). Malware analysis of API calls using FPGA hardware level security. International Journal for Research in Applied Science & Engineering Technology, 7(3), 898–900.
12. Soundappan, S. J. (2023). Designing Intelligent Enterprise Platforms Using Machine Learning Driven API Engineering and Cloud Native Security. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9074-9081.
13. Kale, P. (2024). A Multi-Agent AI Framework for Distributed DevOps Automation and Collaborative Decision-Making in Software Pipelines. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 274-282.
14. Gollapudi, R. (2022). Risk-controlled near-zero-downtime Oracle database migration using GoldenGate. International Journal of Computational and Experimental Science and Engineering, 8(3), 113–123. https://doi.org/10.22399/ijcesen.5382
15. Challa, R. (2023). Reliability Engineering for Zero-Downtime Integration of HPC Systems into Regulated Environments. International Journal of Research and Applied Innovations, 6(6), 10082-10092.
16. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
17. Macha, Y. (2023). Cloud-Based CRM for Healthcare Data Management: A Review of HIPAA Compliance and Validation Rules. TIJER-Int. Res. J, 10(11), 118-123.
18. Umasankar, P., & Saravanan, K. (2016). Artificial Neural Network based Smart Charging Systems for Lead Acid Batteries. Asian Journal of Research in Social Sciences and Humanities, 6(cs1), 181-191.
19. Gujarathi, M. (2023). Active-active data architecture for high-availability enterprise systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(1), 5976–5986.
20. Vasa, M. R. (2024). AI-Augmented Semantic Layer for Governed Fund Data Consolidation and Lakehouse Ingestion. International Journal of Future Innovative Science and Technology (IJFIST), 7(1), 12056.
21. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
22. Koganti, H. (2023). Architecting high-availability Java microservices for financial applications on AWS. International Journal of Science, Research and Technology, 6(3), 9934–9945.
23. Kanji, R. K. (2022). Generative Query Optimization in Data Warehousing: A Foundation Model-Based Approach for Autonomous SQL Generation and Execution Optimization in Hybrid Architectures. Available at SSRN 5401216.
24. Meesala, L. K. (2023). Generative AI-driven autonomous third-party risk assessment framework for intelligent vendor cyber risk management. World Journal of Advanced Research and Reviews, 19(2), 1739-1746.
25. Anand, L., & Neelanarayanan, V. (2020, October). Enchanced multiclass intrusion detection using supervised learning methods. In AIP conference proceedings (Vol. 2282, No. 1, p. 020044). AIP Publishing LLC.
26. Onteddu, A. R., Bandhela, R. R., & Kundavaram, R. R. (2024). Enhancing E-Commerce Product Recommendations through Data Engineering and Machine Learning. Economic Sciences, 20(1), 171-183.
27. 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.
28. Narapareddy, V. S. R., & Yerramilli, S. K. (2023). Artificial intelligence incident forecasting. International Journal of Engineering Technology Research & Management, 7(12), 551–559.
29. Onik, T. A., Irin, K. N., Azam, M. N., Akter, K. S., Nabil, M. A., Hossain, I., & Akter, S. (2023). Artificial Intelligence-Driven Early Detection of Neurological Disorders and Its Implications for Personalized Rehabilitation Strategies. Vascular and Endovascular Review, 6(2), 104-111.
30. Chaba, A. (2018). A platform-independent API integration architecture for scalable enterprise commerce solution. International Journal of Research Publication in Engineering, Technology and Management, 1(1), 9–13.
31. Narayanan, S. (2023). Operationalizing artificial intelligence security in the cloud: A practical integration framework for enterprise risk management. International Journal of Future Innovative Science and Technology (IJFIST), 6(3), 10619.
32. 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.
33. Gummadi, V. P. K. (2023). MuleSoft batch processing: High-volume streaming architecture. Computer Fraud & Security, 50-57.
34. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
35. Sridhar, R., Dhenia, R. N. K., & Kanan, I. J. (2023). A Machine Learning Framework for Predictive Workload Modeling and Dynamic Cloud Resource Allocation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(1), 60-65.
2. 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.
3. Rella, B. P. (2021). Real-time data processing for machine learning: Streaming architectures, challenges, and use cases. IRE Journals, 5(4), 230–236.
4. Meesala, A. (2023). A distributed Kafka-centric framework for high-throughput mid-price computation and intelligent time-series persistence in financial clouds. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN, 2456-3307.
5. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
6. Mohammed, S. (2023). Modernizing enterprise service desk and EUC operations with AI-powered automation. International Journal of Computer Technology and Electronics Communication, 6(6), 8133-8136.
7. Mathew, A., & Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science, Vol. 4, 154-164.
8. Juvvadi, R. R. (2022). Machine learning for anomaly detection in the financial close: A journal entry risk-scoring framework for SAP S/4HANA. International Journal of Communication Networks and Information Security, 14(3), 1684–1695.
9. Potdar, A. (2023). Designing secure sovereign cloud architectures for enterprise data analytics and digital transformation. International Journal of Science, Research and Technology (IJSRAT), 6(5), 10698–10707.
10. Hossain, M. B., Rahman, R., & Hoque, K. (2021). Feature-Driven Supervised Learning for Detecting DDoS Attack. International Journal of Science and Research Archive, 4(01), 393-402.
11. Alex Roney Mathew. (2019). Malware analysis of API calls using FPGA hardware level security. International Journal for Research in Applied Science & Engineering Technology, 7(3), 898–900.
12. Soundappan, S. J. (2023). Designing Intelligent Enterprise Platforms Using Machine Learning Driven API Engineering and Cloud Native Security. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9074-9081.
13. Kale, P. (2024). A Multi-Agent AI Framework for Distributed DevOps Automation and Collaborative Decision-Making in Software Pipelines. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 274-282.
14. Gollapudi, R. (2022). Risk-controlled near-zero-downtime Oracle database migration using GoldenGate. International Journal of Computational and Experimental Science and Engineering, 8(3), 113–123. https://doi.org/10.22399/ijcesen.5382
15. Challa, R. (2023). Reliability Engineering for Zero-Downtime Integration of HPC Systems into Regulated Environments. International Journal of Research and Applied Innovations, 6(6), 10082-10092.
16. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
17. Macha, Y. (2023). Cloud-Based CRM for Healthcare Data Management: A Review of HIPAA Compliance and Validation Rules. TIJER-Int. Res. J, 10(11), 118-123.
18. Umasankar, P., & Saravanan, K. (2016). Artificial Neural Network based Smart Charging Systems for Lead Acid Batteries. Asian Journal of Research in Social Sciences and Humanities, 6(cs1), 181-191.
19. Gujarathi, M. (2023). Active-active data architecture for high-availability enterprise systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(1), 5976–5986.
20. Vasa, M. R. (2024). AI-Augmented Semantic Layer for Governed Fund Data Consolidation and Lakehouse Ingestion. International Journal of Future Innovative Science and Technology (IJFIST), 7(1), 12056.
21. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
22. Koganti, H. (2023). Architecting high-availability Java microservices for financial applications on AWS. International Journal of Science, Research and Technology, 6(3), 9934–9945.
23. Kanji, R. K. (2022). Generative Query Optimization in Data Warehousing: A Foundation Model-Based Approach for Autonomous SQL Generation and Execution Optimization in Hybrid Architectures. Available at SSRN 5401216.
24. Meesala, L. K. (2023). Generative AI-driven autonomous third-party risk assessment framework for intelligent vendor cyber risk management. World Journal of Advanced Research and Reviews, 19(2), 1739-1746.
25. Anand, L., & Neelanarayanan, V. (2020, October). Enchanced multiclass intrusion detection using supervised learning methods. In AIP conference proceedings (Vol. 2282, No. 1, p. 020044). AIP Publishing LLC.
26. Onteddu, A. R., Bandhela, R. R., & Kundavaram, R. R. (2024). Enhancing E-Commerce Product Recommendations through Data Engineering and Machine Learning. Economic Sciences, 20(1), 171-183.
27. 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.
28. Narapareddy, V. S. R., & Yerramilli, S. K. (2023). Artificial intelligence incident forecasting. International Journal of Engineering Technology Research & Management, 7(12), 551–559.
29. Onik, T. A., Irin, K. N., Azam, M. N., Akter, K. S., Nabil, M. A., Hossain, I., & Akter, S. (2023). Artificial Intelligence-Driven Early Detection of Neurological Disorders and Its Implications for Personalized Rehabilitation Strategies. Vascular and Endovascular Review, 6(2), 104-111.
30. Chaba, A. (2018). A platform-independent API integration architecture for scalable enterprise commerce solution. International Journal of Research Publication in Engineering, Technology and Management, 1(1), 9–13.
31. Narayanan, S. (2023). Operationalizing artificial intelligence security in the cloud: A practical integration framework for enterprise risk management. International Journal of Future Innovative Science and Technology (IJFIST), 6(3), 10619.
32. 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.
33. Gummadi, V. P. K. (2023). MuleSoft batch processing: High-volume streaming architecture. Computer Fraud & Security, 50-57.
34. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
35. Sridhar, R., Dhenia, R. N. K., & Kanan, I. J. (2023). A Machine Learning Framework for Predictive Workload Modeling and Dynamic Cloud Resource Allocation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(1), 60-65.
Statistics
106
Views
74
Downloads