INTRODUCING ARTIFICIAL INTELLIGENCE INTO LARGE-SCALE CLOUD-BASED WEB APPLICATIONS: A SCALABLE ARCHITECTURE FOR INTELLIGENT, ADAPTIVE AND RESILIENT WEB SYSTEMS

Abstract

Cloud computing and web-based digital services have grown quickly, and the result is a class of large-scale applications that must keep pace with rising user numbers, unpredictable workloads, many different data sources and demanding availability targets. Most cloud-based web applications still run on predetermined business rules, threshold-based monitoring and manually configured resource management. These mechanisms make it difficult to anticipate workload changes, spot anomalous behaviour, personalize services or adjust computational resources while the system is running. Artificial Intelligence (AI) offers a way to move such applications away from a reactive model and toward systems that are adaptive and able to predict what comes next. This study proposes an AI-enabled architectural framework for that purpose. The architecture connects AI services to cloud-native application components through an intelligent decision layer that combines machine learning, predictive analytics, anomaly detection, resource management and generative AI. It is accompanied by an adapted iterative methodology covering problem definition, data preparation, AI model development, cloud-native integration, security validation, scalability testing and deployment monitoring. Presentation, application, data, AI inference, orchestration and observe-ability functions are kept separate so that the system stays modular and can scale horizontally. The evaluation framework is multi-dimensional, covering response time, throughput, resource utilization, prediction accuracy, scalability, availability, fault recovery, security and cost-efficiency. No experimental measurements from an implemented system were available, so the study presents a proposed architecture and an evaluation framework in which expected outcomes are kept clearly separate from measured results. Throughout, the framework treats responsible AI governance, model lifecycle management, data security and elastic resource provisioning as first-order concerns and it is meant to support later empirical implementation and benchmarking across enterprise, educational, financial and governmental web environments.