AI-Driven Cloud Resource Optimization

This paper addresses a critical challenge in modern cloud computing: optimizing resource allocation across multiple clusters. The use of AI, specifically predictive learning and policy-aware decision-making, offers a proactive approach to resource management, moving beyond reactive methods. This is significant because it promises improved efficiency, faster adaptation to workload changes, and reduced operational overhead, all crucial for scalable and resilient cloud platforms. The focus on cross-cluster telemetry and dynamic adjustment of resource allocation is a key differentiator.

• •Proactive resource management using AI is key to optimizing multi-cluster cloud environments.
• •The framework leverages predictive learning and policy-aware decision-making.
• •Improved resource efficiency, faster stabilization, and reduced performance variability are potential benefits.