# Digital Twin-Driven Virtual Network Architecture for Enhanced Extended Reality Capabilities
## 2 Background and Related Work
### Network Virtualization  
In 2009, a survey related to network virtualization (NV) was presented by [27]. The NV concept evolves from the Virtual Private Network (VPN) field representing an advancement in deploying multiple virtual networks over a common shared physical structure and maintaining isolation among each virtual instance. The advantages of NV include the ability of network operators to re-sell infrastructure to third parties, diversification of infrastructure for private purposes, network sharing and the provisioning of managed services. Network virtualization is emerging as a pivotal technology in the deployment of 5G and beyond networks due to its flexibility and scalability, programmability and interoperability. The most relevant technologies that facilitate an effective network virtualization process are as follows:  
1. Software Defined Network (SDN): A paradigm introduced to decouple the control and data planes in network devices, thus enabling a centralized resource control and provisioning leveraging of an SDN controller. In addition, a SDN introduces dynamic management by enabling applications to directly interact with the hardware resources, while OpenFlow is the protocol to establish communication between the infrastructure elements and SDN controllers [28],
2. Network Function Virtualization (NFV): It addresses a different approach in which network functions are supported. Rather than relying on dedicated hardware, NFV leverages standard servers or cloud-based devices to provide these functions. NFV enables the deployment of efficient and flexible network infrastructures. Moreover, it allows the provision of network operators' dynamic on-demand services, thus enhancing the scalability of the network and optimizing resource utilization [28],
3. Network Slicing: This paradigm deals with the partitioning of a physical network into slices oriented for specific purposes (tailored services). The dynamic allocation of physical resources is achieved by means of cloud computing. The concept of network modularity (divide network functions into modular network functions) can be achieved by offering tailored services leveraged by vertical industries [29].  
[PERSON] [30] states that NV has a crucial role in avoiding network ossification introducing flexible, scalable and technology-agnostic networks and the decoupling of Infrastructure Providers (InPs) and Service Providers (SPs); then, NV enables the possibility to deploy a flexible and scalable Digital Twin Network approached with virtualization techniques. In order to optimize the virtualization process, [PERSON] [31] introduces the Virtual Network Embedding (VNE) concept, which is the process of mapping virtual networks onto a substrate network by means of heuristic and meta-heuristic algorithms and optimization metrics' utilization. The efficient resource mapping algorithms face challenges like NP-hard complexity, heterogeneous environments, scalability and resource constraints, among others; an efficient embedding process is crucial to achieve a DTN's deployment in a constrained resources environment such as a space station.  
Reference [32] addresses the Network Slicing as a Service (NSaaS) concept that can be leveraged to deploy tailored DTNs and improve resource utilization, thus enabling scalability to offer on-demand networks. On the other hand, Reference [33] introduces the virtual security network functions paradigm that can be implemented in Digital Twin Networks to facilitate security device management and deploy security functions empowered with XR technology. Moreover, Reference [34] proposes two paradigms for improving virtual networks that can be leveraged for DTN deployment to introduce a flexible and tailored network: holistic network virtualization in order to introduce network control granularity and end-user virtualization and user-personalized services, and secondly, Pervasive Network Intelligence in order to enable real-time decision making, predictive analysis and self-managed networks.