Adaptive, ML-Enhanced Resource Management for High-Performance Cloud-Based Web Platforms

Efficient resource management in cloud-based web platforms is critical to maintaining performance and cost efficiency under dynamic and unpredictable workloads. This paper proposes a novel resource management framework that integrates predictive workload modeling, multi-tier autoscaling, and cost-aware optimization. The framework utilizes machine learning models to forecast workload patterns and coordinates resource allocation across application, caching, and storage tiers, ensuring minimal latency and optimal resource utilization. Experimental results demonstrate a 45% reduction in mean latency and a 30% decrease in total resource costs compared to traditional threshold-based autoscaling. The framework also improves resource utilization to 85% on average while halving the frequency of scaling actions, reducing operational instability. These outcomes highlight the effectiveness of the proposed approach in balancing performance and cost objectives in complex cloud environments. The proposed framework advances the state of the art in cloud resource management by addressing inter-tier dependencies and leveraging predictive analytics for proactive scaling. Its adaptability to diverse workload patterns and potential applicability to multi-cloud and edge computing scenarios make it a scalable and robust solution for modern web platforms.