A Dynamic Approach to Optimizing Cloud Resource Allocation for Enhanced E-commerce Performance

This study tackles the challenge of enhancing Ecommerce performance on the Shopping platform through a dynamic approach to optimizing cloud resource allocation. The problem at hand involves the need for scalable infrastructure to accommodate fluctuating demand, ensuring seamless user experiences during peak periods. Our proposed method employs a sophisticated dynamic allocation algorithm that adapts in real-time to varying workloads. The system’s flow begins with continuous monitoring of platform activity, swiftly identifying resource demands. Leveraging machine learning, the algorithm predicts resource needs, enabling proactive scaling. Results demonstrate a significant improvement in platform responsiveness, reducing latency during traffic spikes and ensuring optimal performance. This dynamic approach not only addresses the resource allocation challenge but also provides a solution for E-commerce platforms, fostering enhanced user satisfaction and scalability.