Optimizing Performance in Modern Web Systems and Applications: An Analysis of Caching and Load Balancing Techniques

Ebtehal Akeel Hamed^1,*, Ahmed Mahdi Abdulkadium², Enas Faris Yahya³

¹College of Physical Education and Sport Sciences, Al Qasim Green University, Babylon 51013, Iraq

²College of Education, Al Qasim Green University, Babylon, 51013, Iraq

³Department of Computer Science, College of Science, Al-Nahrain University, Baghdad, Iraq

Emails: ebtehal82@uoqasim.edu.iq; ahmed_mahdi@uoqasim.edu.iq; enasfaris2007@nahrainuniv.edu.iq

Abstract

To increase scalability, response speed, and fault tolerance, modern web systems must have load balancing and caching solutions. Better resource allocation and traffic management control help to prevent system overload. This is essential to satisfy the growing need for perfect digital experiences. This work intends to demonstrate an adaptive load balancing system using real-time job scheduling, predictive analytics, and multi-layer caching, integrating artificial intelligence technology. Our hybrid deep learning and storage systems lower data retrieval time and estimate traffic. This approach tremendously increases the efficiency of online systems. Unlike conventional load balancing systems, which rely on either static or rule-based traffic distribution, our approach employs artificial intelligence-based dynamic allocation to real-time resource adjustment. Our solution forecasts workload surges and pre-allocated resources suitably using deep neural networks in conjunction with past traffic data. To hasten data retrieval, the multi-layer caching approach makes use of content delivery networks (CDNs) and cloud-based storage. This lessens the double effort required and helps one discover objects more easily. Among the several advantages, the new approach offers over the old ones are a 40% decrease in energy use, a 20% improvement in resource use, and a 50% improvement in reaction time. This approach has exceeded round robin and dynamic load balancing in actual AWS simulations. These findings highlight how incorporating predictive analytics driven by artificial intelligence might improve current site designs. For cloud platforms, IoT systems, and high-traffic online applications needing efficiency and fast adaption, this approach performs well.

Keywords: Adaptive Load Balancing; AI-Driven Optimization; Cloud Scalability; Deep Learning Forecasting; Dynamic Resource Allocation; Edge Computing; Fault-Tolerant Systems; Predictive Analytics; Sustainable Computing