Leveraging AI and Machine Learning for Performance Optimization in Web Applications
DOI:
https://doi.org/10.36676/dira.v12.i2.85Keywords:
web environment, traffic across servers, web technologies, scalability, Learning (ML), Resource managementAbstract
The rapid evolution of web technologies has placed an unprecedented demand on web applications to deliver high performance, scalability, and responsiveness. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools that can significantly optimize the performance of web applications. This paper explores the integration of AI and ML techniques in enhancing various aspects of web application performance, including load balancing, caching, resource management, and user experience personalization. The deployment of AI-driven algorithms enables real-time monitoring and predictive analytics, allowing for proactive adjustments to prevent bottlenecks and ensure smooth operation under varying loads.
One of the primary areas where AI and ML contribute is in load balancing. Traditional load balancing techniques often rely on static or rule-based methods, which may not adapt well to dynamic web environments. AI-powered load balancers, however, can learn from traffic patterns and predict surges in demand, enabling more efficient distribution of traffic across servers. This adaptive approach not only improves response times but also reduces the risk of server overloads, enhancing the overall reliability of web applications.
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