Akademik Veri Yönetim Sistemi
IEEE Communications Surveys and Tutorials, 2025 (SCI-Expanded, Scopus)
Digital Twin-as-a-Service (DTaaS) has emerged as a transformative paradigm to deliver modular, scalable, and intelligent digital twin (DT) functionalities. Regarding this, several studies have investigated DT platforms and their enabling technologies. However, their focus mostly remains domain-specific, thereby limiting the potential for generalisation across diverse contexts. Furthermore, these studies lack a systematic framework to address both architectural elements and their performance evaluation required for an end-to-end DTaaS platform. To bridge this gap, in this survey, we present a comprehensive component-based taxonomy for DTaaS platforms, categorised into core and supportive components. As the core components, we review Data, Model, Service, and Communication, whereas the supportive components include Infrastructure, Orchestration, and Security elements. For each component, we analyse the state-of-the-art methods, enabling technologies, and associated design trade-offs. We further introduce a metric-driven perspective to design measurable and context-aware DTaaS platforms. In this regard, we present several DT performance metrics, including twinning rate, twin fidelity, and age-of-twin and map them to DTaaS components. To demonstrate the applicability of our framework, we present a case study based on our own DTaaS platform. We perform two metric-driven implementations, (i) Radio Access Network (RAN) management and (ii) Distributed Denial of Service (DDoS) attack detection by exemplifying how DT-metrics can be integrated with the DTaaS components. Also, we outline key open issues for context-aware, interoperable, and secure DTaaS platforms. By jointly addressing an end-to-end architectural design and performance metrics, this survey demonstrates a foundational reference for the structured development, evaluation, and operation of DTaaS platforms.