Self-Optimizing Angular Applications: A Novel Framework for AI-Driven Performance Adaptation in Production Environments
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P112Keywords:
Self-Optimizing Systems, Angular Framework, AI-Driven Optimization, Machine Learning, Runtime Optimization, Client-Side Intelligence, Predictive Optimization, Dynamic Code Splitting, Intelligent Lazy Loading, Real-Time Adaptation, User Experience Optimization, Web Application Performance, Cloud-Native Applications, Devops Integration, Telemetry-Driven Optimization, Performance Monitoring, Distributed Tracing, CI/CD Pipelines, Scalable Infrastructure, Cloud ObservabilityAbstract
Modern web applications must maintain high performance and responsiveness despite increasing complexity, diverse user environments, and fluctuating workloads. Angular has become a popular frontend framework to create scalable single-page applications; nevertheless, performance optimization in Angular application is usually done manually by relying on the approach of the static method of lazy loading, code splitting, and change detection optimization. Such conventional practices have a shortcoming in their capability to dynamically adjust to the dynamics of changing production conditions. In order to overcome this issue, the current paper will suggest an innovative AI-based self-optimizing Angular application that allows smart performance adaptation in real-time. The suggested framework combines the functions of real-time telemetry gathering and machine learning performance analysis with adaptive optimization mechanisms in order to automatically identify and alleviate performance bottlenecks. The metrics like CPU usage, memory usage, component rendering and user interaction behavior are constantly measured by browser performance APIs and logging systems. Such telemetry data are analyzed using an AI analytics application, which uses predictive models and reinforcement learning methods to discover performance problems and identify the most effective optimization solutions. A dynamic optimization engine is a dynamic engine that uses adaptive optimization, which modifies application configurations such as resource allocation, change detection policy, and module loading policy. Through experimental analysis, it was shown that the suggested framework greatly enhances the startup time of applications, throughput efficiency and scalability relative to the conventional manual optimization strategies. The framework ensures that maintenance overhead is minimized and that the reliability of the Angular applications in production is improved by allowing autonomous performance management. The findings show the possibility of integrating artificial intelligence and the current web architectures in the production of intelligent, self-adapting software systems that can be used to sustain good performance under changing operational environments.
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