Abstract

The performance of modern websites has become a critical factor in determining user engagement, service reliability, and computational efficiency. Background studies have shown that increasing traffic load, dynamic content delivery, and heterogeneous user behavior have significantly reduced responsiveness and scalability of web systems. The problem of inefficient resource utilization and delayed response time has remained persistent, especially in cloud-hosted and data-intensive web environments. To address this issue, a Hybrid Deep Reinforcement Learning and Gradient Boosted Optimization (HDRL-GBO) method has been proposed to improve website performance through adaptive resource allocation and predictive load balancing. The proposed HDRL-GBO method integrated deep reinforcement learning for real-time decision optimization and gradient boosted machine learning for performance prediction. The reinforcement component dynamically adjusted caching strategies, request routing, and server allocation, while the boosting model estimated workload patterns and latency variations. The system has been trained using historical traffic logs and real-time simulation data. The proposed HDRL-GBO framework has significantly reduced average latency to 85–140 ms, compared to 105–240 ms in baseline methods. Throughput has improved to 520–600 req/s, while existing methods achieve 340–470 req/s. CPU utilization has reached 92% balanced efficiency, outperforming EHGA at 90% and PSO at 88%. These results confirm that the proposed system has improved performance stability, scalability, and response efficiency under dynamic web traffic conditions.

Authors

P.C. Geethu¹, Deena Jose², N.M. Shejina³, M.B. Meethu⁴
Federal Institute of Science and Technology, India^1,2, IES College of Engineering, India^3,4

Keywords

Website Optimization, Deep Learning, Machine Learning, Reinforcement Learning, Web Performance