Digital Twin Architecture for Edge-to-Cloud Infrastructure Monitoring and AI-Driven Predictive Operations
DOI:
https://doi.org/10.15662/IJARCST.2025.0806033Keywords:
Digital Twin, Edge Computing, Cloud Infrastructure, Artificial Intelligence, Predictive Operations, Infrastructure Monitoring, Internet of Things, Machine Learning, Autonomous Management, Smart Computing SystemsAbstract
The rapid expansion of distributed computing environments has created significant challenges in monitoring, managing, and optimizing modern edge-to-cloud infrastructures. Traditional infrastructure monitoring approaches often rely on isolated tools that provide limited visibility into complex interactions among edge devices, communication networks, cloud platforms, and artificial intelligence-driven applications. Digital twin technology offers a transformative approach by creating dynamic virtual representations of physical infrastructure assets, enabling real-time observation, simulation, prediction, and autonomous decision-making. This research explores a digital twin architecture designed for edge-to-cloud infrastructure monitoring and AI-driven predictive operations. The proposed architecture integrates Internet of Things (IoT) sensors, edge computing nodes, cloud platforms, data analytics frameworks, machine learning models, and visualization mechanisms to establish continuous synchronization between physical and virtual environments. The study examines how artificial intelligence techniques can enhance anomaly detection, failure prediction, resource optimization, and automated operational management. A research methodology based on architectural analysis, system modeling, data-driven evaluation, and performance assessment is proposed to investigate the effectiveness of digital twins in improving infrastructure reliability and operational efficiency. The research highlights the role of digital twins as an intelligent coordination layer between edge and cloud environments, supporting proactive maintenance, adaptive resource allocation, and autonomous infrastructure management in next-generation computing ecosystems
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