AI-DRIVEN OPTIMIZATION FOR MULTI-TENANT CLOUD PLATFORMS: BALANCING COST, PERFORMANCE, AND SECURITY
Keywords:
Multi-tenant Cloud Computing, Artificial Intelligence Optimization, Cloud Security Management, Resource Allocation, Predictive AnalyticsAbstract
This article explores the challenges and solutions in managing multi-tenant cloud platforms through an innovative AI-driven optimization framework. The article addresses critical issues balancing cost efficiency, performance optimization, and security enforcement in cloud environments. The proposed framework leverages predictive analytics and machine learning models to allocate resources and maintain security boundaries dynamically across distributed systems. Through comprehensive case studies involving major platforms like Databricks and AWS, the research demonstrates significant improvements in resource utilization, cost reduction, and security enforcement. The findings contribute valuable insights into the effectiveness of AI-driven approaches in cloud resource management and establish a foundation for future developments in intelligent cloud optimization strategies.
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