## 2 Background and Related Work
Primarily, progress in technologies related to _data acquisition and analysis_ (such as advanced wireless networks, communication protocols, and big data analytics) facilitates the creation of accurate digital representations and seamless integration with their corresponding physical entities [16; 17]. _High-fidelity modeling_ involves both the precise translation of raw data from the physical asset into knowledge and the integration of information generated by the virtual model, with the aim of optimizing the physical entity [18]. In that respect, AI is frequently employed to create models that utilize established inputs and outputs acquired from the real-world system, aiding in the comprehension of how physical properties interact with each other [19]. _Simulation_ stands out as a significant enabler of the DTs, mainly due to the heightened value it provides through facilitating seamless real-time communication between virtual and physical assets. Simulating DT behavior opens up substantial opportunities for the mutual optimization of the virtual and physical models in terms of operational efficiency and maintenance schedules [20]. In the aim of establishing a _comprehensive framework_ for constructing DTs, various endeavors have emerged to devise distinct modeling approaches. These efforts involve creating various DT models organized into methodological tiers, referred to as layers, to enable smooth information exchange between the physical and virtual realms. In the literature, commonly encountered modeling methods include the five-layer structure, six-layer structure, three-step process, and five-dimensional modeling [21]. In Ref. [22], the authors developed a DT reference model and architecture, and applied them in an industrial case. Drawing inspiration from the RAMI 4.0 reference model [5], they introduced a layered model consisting of three coordinated dimensions that encompass essential aspects of DTs, namely architecture, value life cycle, and integration. The literature also delves into efforts to devise a methodology for DT development. [PERSON]. [23] proposed a methodology design using model-driven engineering (MDE) that strives toward being both flexible and generic. In this approach, a DT is initially conceptualized as a composition of fundamental components offering core functionalities (such as identification, storage, communication, security, etc.). Subsequently, an aggregated DT is characterized as a hierarchical composition of other DTs. A generic reference architecture based on these principles and a practical implementation methodology are put forward utilizing AutomationML [24]. In this paper, we present a DT model, a reference architecture, and a development methodology to help companies, both big and SME, leverage the potential of the DT paradigm in the aim of boosting their digitization process. The proposed DT model em bodies all the state-of-the-art approaches, i.e., the data-driven, the model-driven and the hybrid ones, and accounts for the possibility of having the DT distributed over the industrial cloud continuum (OT-to-Cloud). Similarly to Ref. [22], our reference IT architecture inspires to the principles of the RAMI 4.0 architecture; besides that, we propose an implementation of a software prototype of a DT-based PaaS for concrete use by the developers. Finally, we deliver a methodology and a set of practical guidelines for the implementation and TOSCA-based deployment of complex, containerized, and distributed DTs in the computing continuum.