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@article{207010,
        author = {Chinthan Gowda G and Thrupthi L and Sujay S},
        title = {Unified Multicloud Resource Management Framework (UMRMF): An AI-Driven Approach for Cost Optimization, Governance, and Workload Scheduling},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {3741-3748},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207010},
        abstract = {With exponential growth of cloud computing, enterprises have started using multicloud environment in which services and resources reside at multiple cloud service providers (CSPs). The advantages of using such systems include increased flexibility, fault tolerance, better performance and avoiding vendor lock-in. Each CSP is designed and implemented based on its unique architectures, APIs, pricing model, security mechanisms and governance rules. Interoperability issues, cost optimization, security governance, automated provisioning and scheduling of tasks are some of the major challenges faced by users while integrating and managing their resources in this environment. In this paper, we provide a detailed analysis of the issues in resource management within multicloud environments. In addition to providing a thorough overview of the research done in the area of multicloud management, we present the proposed Unified Multicloud Resource Management Framework (UMRMF) that coordinates and manages the resources residing in different cloud environments. The UMRMF is an innovative framework that includes the compatibility layer, automated enforcement of governance rules and AI-powered cost optimization modules. Experiments performed on real world data from Azure and Google cloud proved that the proposed solution is effective in achieving lower operational overhead, higher utilization of resources and reduced cost of multicloud operations (up to 34% and 22.8% respectively).},
        keywords = {Multicloud, Resource Management, Cloud Interoperability, Cost Optimization, Workload Scheduling, Policy Governance, LSTM, Pareto Optimization, FinOps, Observability.},
        month = {July},
        }