Authors: Priya S. Bhatia
Abstract: Digital twin (DT) technology virtual replicas of physical assets, systems, or processes has emerged as a transformative approach to optimizing enterprise cloud infrastructure. By enabling real-time data integration, predictive modeling, and adaptive simulations, digital twins provide organizations with unprecedented visibility and control over cloud environments. This review explores how digital twin simulations influence the performance, scalability, cost-efficiency, and resilience of enterprise cloud systems. It examines the underlying principles, enabling technologies, and industrial implementations that drive this integration. Through a synthesis of recent research and case studies, the paper identifies how DTs enhance decision-making by forecasting workloads, detecting failures before they occur, and recommending optimal configurations for resource utilization. Additionally, the review highlights how digital twins enable dynamic cloud scaling, energy optimization, and service reliability through continuous feedback loops and machine learning-driven simulations. However, challenges remain, including interoperability issues, high computational costs, and cybersecurity risks. The paper also presents emerging trends such as edge-cloud digital twins, AI-driven automation, and sustainable infrastructure optimization. The findings underscore that digital twin simulations are not just tools for operational efficiency but strategic enablers of intelligent, autonomous cloud ecosystems. Ultimately, the review provides a foundation for future research into standardization, security, and advanced analytics in twin-driven cloud infrastructures.
DOI: http://doi.org/10.5281/zenodo.17839944
