Citation:

Muthu Selvam, PrakasamVenkatachalam, Sathiskumar Meganathan, Thalapathi Rajasekaran R, 2025. "Implementing AI-Driven Micro-Frontend Architectures Using Reinforcement Learning and Graph Neural Networks for Scalable and Maintainable Large-Scale Web Applications", ESP Journal of Engineering & Technology Advancements  5(1): 41-55.

Abstract:

The rapid evolution of web applications across diverse domains like e-commerce, healthcare, and enterprise solutions necessitates architectures that are scalable, maintainable, and performance-efficient. Micro-Frontends (MFEs) have emerged as a modular alternative to monolithic frontends, enabling independent development, deployment, and testing of UI components. However, challenges such as dependency conflicts, static orchestration strategies, and inefficient module management limit the full potential of traditional MFE architectures. This research introduces Dynamic AI-Orchestrated Modular Architecture (DAIMA), a novel framework that leverages artificial intelligence to enhance the scalability and maintainability of large-scale applications. DAIMA incorporates two key innovations: Reinforcement Learning for Dynamic Orchestration, which employs deep Q-learning networks to optimize module loading sequences in real time based on user behavior patterns, and Graph Neural Networks (GNN)-Enhanced Dependency Management, which proactively resolves dependency conflicts through advanced graph analysis. These AI-driven mechanisms enable DAIMA to dynamically manage modules, reduce latency, and ensure seamless compatibility across micro-frontend environments. Experimental evaluations demonstrate that DAIMA achieves a 30% improvement in page load times and reduces dependency-related conflicts by 40%, outperforming existing static orchestration and manual dependency management solutions. Comparative studies further underscore the framework's ability to deliver personalized user experiences and streamline modular development workflows. Future research will explore integrating federated learning for privacy-preserving data analysis and extending DAIMA's capabilities to edge computing environments. The proposed framework signifies a pivotal step towards AI-enhanced web application architectures, addressing critical scalability and maintainability challenges.

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Keywords:

Micro-Frontends, Artificial Intelligence, Reinforcement Learning, Graph Neural Networks, Scalable Web Architectures, Modular Development.