همايش ملی پيشرفت های معماری سازمانی

صفحه اصلی / نهمین همایش پیشرفت‌های معماری سازمانی ایران

Integration of AI-Augmented CloudOps with EAM and IT4IT: A Strategic–Operational Framework for Intelligent Cloud Infrastructure Management

نویسندگان :

Maryam Mayabi Joghal¹ Alireza Shameli-Sendi²

1- Shahid Beheshti University 2- Shahid Beheshti University

کلمات کلیدی :

Enterprise Architecture Management (EAM)،IT4IT Framework،Intelligent Cloud Operations،AI-Augmented CloudOps،Software-Defined Infrastructure،Digital Transformation،Value Stream

چکیده :

In the era of digital transformation, Enterprise Architecture Management (EAM) is no longer merely a high-level technology blueprint; rather, it must evolve into a dynamic framework capable of strategically and value-driven integration of emerging technologies throughout the IT lifecycle. This paper explores the role of intelligent CloudOps (AI-Augmented CloudOps) within this framework, with a particular focus on integrating EAM with the IT4IT operating model. In this approach, AI-Augmented CloudOps is not analyzed solely as a technical tool but from two key perspectives. First, As an operational capability within the IT4IT value stream responsible for monitoring, detecting, and autonomously correcting issues in cloud services—a process commonly referred to as Detect to Correct. By leveraging artificial intelligence, operations are elevated from reactive to predictive and automated. Second, As a standalone digital product, whose lifecycle is managed across the four IT4IT value streams: Strategy to Portfolio (S2P), Requirement to Deploy (R2D), Request to Fulfill (R2F), and Detect to Correct (D2C). The proposed model has been implemented in a real-world organization. In the S2P phase, the need for a secure, dedicated, and sovereign cloud infrastructure was defined as a strategic digital product. In the R2D phase, a fully automated and intelligent deployment system based on OpenStack and Ceph was developed, enabling both optimal architecture recommendations and offline deployment. In the R2F phase, the infrastructure was delivered as a self-service offering to internal teams, ensuring automatic compliance with enterprise architecture. Finally, in the D2C phase, predictive analytics and intelligent self-healing mechanisms enhanced service resilience, while creating a closed feedback loop back to the S2P phase. The results demonstrate significant reductions in incident response time, downtime, and operational costs. Moreover, the model enables the delivery of stable, secure, and governance-compliant digital services—particularly critical in highly regulated industries—while also addressing challenges such as technical complexity, the need for specialized expertise, and ensuring security in offline environments.