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@article{191692,
        author = {Aruna Kumari and Divyanshi Rathore},
        title = {AI Optimization in Cloud Computing: A Multi-Layered Strategy for Performance, Cost, and Sustainability},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {34-42},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191692},
        abstract = {The rapid advancement of Artificial Intelligence (AI), particularly Generative AI (GenAI), has amplified the computational, financial, and environmental demands of large-scale deployments. This paper presents a comprehensive analysis of strategies to optimize AI workloads within cloud computing environments. It emphasizes a tri-dimensional framework that integrates computational performance, economic efficiency (FinOps), and environmental sustainability (Green AI) as core pillars of responsible AI scaling. The study explores how Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Function-as-a-Service (FaaS) models can be effectively leveraged to balance control, flexibility, and cost efficiency across diverse AI workloads. In particular, it highlights the growing need for automated, AIdriven operations (AIOps) to complement financial governance (FinOps), given the non-linear cost structures and operational complexities of modern GenAI systems. By linking performance tuning, cost optimization, and carbon-conscious design, the report underscores that AI optimization must be treated as an integrated, recursive process—where AI is employed to manage and enhance the infrastructure that powers AI itself. This holistic perspective aims to guide organizations toward scalable, economically viable, and environmentally responsible AI deployment strategies in the era of accelerated computational growth.},
        keywords = {AIOps, Artificial Intelligence, Cloud Computing, Computational Performance, FinOps, Generative AI, Green AI, Infrastructure Optimization, Machine Learning Operations, Scalability, Sustainability.},
        month = {},
        }