AI-DRIVEN CLOUD COST OPTIMIZATION STRATEGIES FOR LARGE-SCALE MULTI-REGION INFRASTRUCTURE PLATFORM
DOI:
https://doi.org/10.15662/x3xt1m35Keywords:
Cloud Computing, Artificial Intelligence, Machine Learning, Cost Optimization, Multi-Region Systems, Finops, Auto-ScalingAbstract
Cloud computing has revolutionized how organizations develop and operate their applications based on the scalable and adaptable infrastructure services. Nevertheless, as large-scale multi-region cloud architectures are becoming more and more popular, the operational costs have become a significant issue to manage. Such environments entail an unstable workload, a geographically distributed resource base, and sophisticated pricing models, which tend to result in poor use of resources and high spending. AI technology is essential in helping to overcome these challenges because it can be used to perform predictive analytics, automated decision-making, and ongoing optimization. The paper will conduct a detailed theoretical discussion of the AI-driven cloud cost optimization, including all the key concepts and models, algorithms and practical strategies. In and real world research results of cost savings as high as 40 percent and service level agreements (SLAs).
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