Improving Web Platform Performance Through Predictive Cloud Resource Management

Optimizing resource management is essential for improving the performance of web platforms in dynamic cloud environments. This paper introduces a predictive cloud resource management framework that integrates Long Short-Term Memory (LSTM) neural networks for workload forecasting with a dynamic resource scaling mechanism. The framework proactively allocates cloud resources, addressing challenges such as latency, cost inefficiency, and under-utilization. Experimental evaluations on a benchmark dataset demonstrated significant performance improvements over traditional methods. The proposed framework achieved a mean squared error (MSE) of 0.012 in workload prediction, a 28% reduction in response time, and a 23% increase in cost savings. These results highlight the effectiveness of predictive resource management in managing dynamic workloads and optimizing distributed web systems. The findings provide a scalable and cost-efficient solution, contributing to advancements in cloud-based resource management. Future work will explore hybrid models to further enhance adaptability and address real-time anomalies. This research lays a foundation for developing innovative and sustainable strategies in distributed computing environments.