Enterprise Architecture Frameworks for Cloud Transformation: Aligning Business Strategy with Cloud Migration Goals
Keywords:
Enterprise Architecture, cloud transformationAbstract
The rapid adoption of cloud computing has fundamentally altered how organizations manage, store, and process data, requiring a shift from traditional IT infrastructure models to flexible, scalable cloud-based solutions. As enterprises embark on cloud migration journeys, it becomes critical to align their cloud transformation initiatives with overarching business goals, ensuring that technological advancements directly support strategic objectives. Enterprise Architecture (EA) frameworks offer a structured approach to this alignment, enabling organizations to bridge the gap between business strategy and technological capabilities. This paper investigates the role of EA frameworks in facilitating cloud transformation, exploring how these frameworks can be adapted or expanded to support cloud-specific needs and challenges. Traditional EA frameworks, such as TOGAF, Zachman, and DoDAF, are well-established in guiding IT and business alignment, yet their adaptation to cloud environments requires a nuanced understanding of cloud-native paradigms, hybrid configurations, and emerging service models. The study emphasizes the need for dynamic and agile EA practices that accommodate the unique operational and strategic demands posed by cloud transformation, including service modularization, interoperability, and cross-functional integration.
In particular, the research highlights the key components and principles of EA frameworks that can be leveraged to ensure a smooth transition to the cloud while maintaining alignment with business priorities. One focal point is the capability of EA to address complexities associated with multi-cloud and hybrid cloud environments, as well as the integration of cloud-based services with legacy systems. Furthermore, the paper examines the role of EA in facilitating governance, risk management, and compliance in cloud environments, areas that are essential yet often underestimated in cloud adoption strategies. By establishing standardized processes and protocols, EA frameworks can mitigate risks associated with data security, privacy, and regulatory compliance, which are exacerbated in distributed and multi-tenant cloud architectures.
This paper also explores case studies of enterprises that have effectively used EA frameworks to navigate their cloud transformation, presenting best practices and lessons learned. These case studies illustrate how specific EA components, such as Business Architecture, Information Systems Architecture, and Technology Architecture, can be adapted to the cloud context. The findings suggest that, when applied effectively, EA frameworks can enhance decision-making processes, optimize resource allocation, and streamline the adoption of cloud services, thereby contributing to improved agility, operational efficiency, and competitiveness. Additionally, the study identifies gaps in traditional EA frameworks with respect to cloud-specific considerations and proposes enhancements to better support cloud transformation.
Downloads
References
M. A. Hossain, A. Al-Amin, and S. K. Qureshi, "A Survey on Enterprise Architecture Frameworks: Analysis and Comparative Study," Procedia Computer Science, vol. 75, pp. 189-196, 2015.
Ratnala, Anil Kumar, Rama Krishna Inampudi, and Thirunavukkarasu Pichaimani. "Evaluating Time Complexity in Distributed Big Data Systems: A Case Study on the Performance of Hadoop and Apache Spark in Large-Scale Data Processing." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 732-773.
Sangaraju, Varun Varma, and Kathleen Hargiss. "Zero trust security and multifactor authentication in fog computing environment." Available at SSRN 4472055.
Machireddy, Jeshwanth Reddy. "ARTIFICIAL INTELLIGENCE-BASED APPROACH TO PERFORM MONITORING AND DIAGNOSTIC PROCESS FOR A HOLISTIC ENVIRONMENT." International Journal of Computer Science and Engineering Research and Development (IJCSERD) 14.2 (2024): 71-88.
Tamanampudi, Venkata Mohit. "AI-Driven Incident Management in DevOps: Leveraging Deep Learning Models and Autonomous Agents for Real-Time Anomaly Detection and Mitigation." Hong Kong Journal of AI and Medicine 4.1 (2024): 339-381.
S. Kumari, “Cloud Transformation and Cybersecurity: Using AI for Securing Data Migration and Optimizing Cloud Operations in Agile Environments”, J. Sci. Tech., vol. 1, no. 1, pp. 791–808, Oct. 2020.
Kurkute, Mahadu Vinayak, Anil Kumar Ratnala, and Thirunavukkarasu Pichaimani. "AI-Powered IT Service Management for Predictive Maintenance in Manufacturing: Leveraging Machine Learning to Optimize Service Request Management and Minimize Downtime." Journal of Artificial Intelligence Research 3.2 (2023): 212-252.
Pichaimani, T., Inampudi, R. K., & Ratnala, A. K. (2021). Generative AI for Optimizing Enterprise Search: Leveraging Deep Learning Models to Automate Knowledge Discovery and Employee Onboarding Processes. Journal of Artificial Intelligence Research, 1(2), 109-148.
Surampudi, Yeswanth, Dharmeesh Kondaveeti, and Thirunavukkarasu Pichaimani. "A Comparative Study of Time Complexity in Big Data Engineering: Evaluating Efficiency of Sorting and Searching Algorithms in Large-Scale Data Systems." Journal of Science & Technology 4.4 (2023): 127-165.
Kondaveeti, Dharmeesh, Rama Krishna Inampudi, and Mahadu Vinayak Kurkute. "Time Complexity Analysis of Graph Algorithms in Big Data: Evaluating the Performance of PageRank and Shortest Path Algorithms for Large-Scale Networks." Journal of Science & Technology 5.4 (2024): 159-204.
Tamanampudi, Venkata Mohit. "Generative AI Agents for Automated Infrastructure Management in DevOps: Reducing Downtime and Enhancing Resource Efficiency in Cloud-Based Applications." Journal of AI-Assisted Scientific Discovery 4.1 (2024): 488-532.
Inampudi, Rama Krishna, Thirunavukkarasu Pichaimani, and Yeswanth Surampudi. "AI-Enhanced Fraud Detection in Real-Time Payment Systems: Leveraging Machine Learning and Anomaly Detection to Secure Digital Transactions." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 483-523.
Sangaraju, Varun Varma, and Senthilkumar Rajagopal. "Applications of Computational Models in OCD." In Nutrition and Obsessive-Compulsive Disorder, pp. 26-35. CRC Press.
S. Kumari, “Cybersecurity Risk Mitigation in Agile Digital Transformation: Leveraging AI for Real-Time Vulnerability Scanning and Incident Response ”, Adv. in Deep Learning Techniques, vol. 3, no. 2, pp. 50–74, Dec. 2023
Parida, Priya Ranjan, Rama Krishna Inampudi, and Anil Kumar Ratnala. "AI-Driven ITSM for Enhancing Content Delivery in the Entertainment Industry: A Machine Learning Approach to Predict and Automate Service Requests." Journal of Artificial Intelligence Research and Applications 3.1 (2023): 759-799.
M. K. Toosi, A. G. Dastjerdi, and R. Buyya, "Cloud Computing: Principles and Paradigms," IEEE Cloud Computing, vol. 1, no. 2, pp. 26-34, 2014.
J. L. Zachman, "A Framework for Information Systems Architecture," IBM Systems Journal, vol. 26, no. 3, pp. 276-292, 1987.
The Open Group, "TOGAF 9.2: The Open Group Architecture Framework," The Open Group, 2018. [Online]. Available: https://www.opengroup.org/togaf.
M. B. A. Janiesch, M. Maedche, and S. K. K. B. Soh, "Cloud Computing and Enterprise Architecture: Challenges and Opportunities," International Journal of Cloud Computing and Services Science, vol. 4, no. 3, pp. 221-239, 2015.
Y. F. Papageorgiou, "Cloud Computing Adoption in Enterprise Architecture Frameworks: A Survey," International Journal of Cloud Computing and Services Science, vol. 5, no. 3, pp. 192-204, 2016.
M. E. Leclercq, "Cloud Migration Strategy Framework," International Journal of Cloud Computing and Technology, vol. 6, no. 1, pp. 24-31, 2017.
A. R. Alam, "Challenges in Cloud Computing: Migrating Enterprise Architecture Models," International Journal of Computer Applications, vol. 47, no. 14, pp. 1-9, 2012.
B. T. Tan, "Cloud Governance Framework for Enterprise Architectures," International Journal of Enterprise Architecture and Technology, vol. 8, no. 1, pp. 45-58, 2017.
P. H. Zhang, "Cloud Computing and Enterprise Architecture: From Integration to Innovation," IEEE Transactions on Cloud Computing, vol. 9, no. 1, pp. 1-12, 2021.
T. Kim, "Multi-Cloud Computing Models and Enterprise Architecture," Proceedings of the 2019 IEEE International Conference on Cloud Computing (CLOUD), pp. 315-324, 2019.
P. M. Harris, "Enterprise Architecture for Cloud Computing: A Roadmap," Journal of Cloud Computing, vol. 6, no. 2, pp. 124-137, 2015.
J. Roy, "Evolving Enterprise Architectures in Cloud Computing Environments: Challenges and Solutions," Computers in Industry, vol. 92, pp. 21-33, 2018.
S. Brown, "The Role of Cloud Computing in Enterprise Architecture," International Journal of Information Management, vol. 48, pp. 94-102, 2019.
Z. B. Zhang, "Cloud and Enterprise Architecture: Integrating Governance, Security, and Compliance," Cloud Computing for Enterprise Architectures, pp. 153-171, Springer, 2021.
S. Y. Wu, "Cloud Governance and Compliance Frameworks: The Role of EA in Cloud Transformation," International Journal of Cloud Computing, vol. 7, no. 3, pp. 233-246, 2020.
T. Chien, "Building the Future of Cloud Enterprise Architecture," Proceedings of the 2020 IEEE Cloud Computing Conference, pp. 78-89, 2020.
G. Liu, "Designing EA Frameworks for Cloud Integration," International Journal of Enterprise Information Systems, vol. 14, no. 2, pp. 53-67, 2020.
K. H. Liu, "Cloud Architecture and Enterprise Integration Frameworks," Journal of Cloud Computing: Advances, Systems, and Applications, vol. 9, no. 1, pp. 101-110, 2022.
R. Harris, "A Survey of Cloud Architecture Frameworks: Benefits, Challenges, and Future Directions," Journal of Cloud Computing and Technology, vol. 8, no. 3, pp. 1-12, 2021.
