# Architectural Design for Digital Twin Networks
## 5 Design and Implementation Strategies
### Data Collection  
Data collection forms the bedrock of a DTN, and its effective implementation is crucial for achieving the network's operational goals. This section explores key considerations and methodologies involved in the collection, storage, and management of data within a DTN, drawing insights from both established protocols and innovative solutions tailored to specific network environments. Telemetry (in-band or out-of-band) plays a crucial role in gaining insights into network performance and its operational state. By continuously collecting and analyzing data, it offers real-time visibility into various performance metrics and system health indicators, enabling proactive management and optimization of the network.  
Implementing data collection in a DTN requires careful consideration of existing protocols and tools, as displayed in Figure 3, that facilitate unified and efficient data gathering. Protocols like SNMP, NETCONF, and IPFIX are commonly utilized in IP-based networks for their robustness and interoperability [33; 34; 35]. However, the applicability of these protocols may vary depending on the underlying network technology and specific goals of the DTN. For instance, in scenarios where standardized tools are lacking, custom protocols inspired by existing frameworks need to be designed to meet specific data collection needs while aligning with network structure requirements.  
Many existing works in the literature, such as [18; 19; 21; 23; 25], often simplify data acquisition processes or rely on fixed datasets without elaborating on the methodology. Note that simulator data can often exceed the amount of information retrievable from a real network, due to the increased control and centralization they offers. Therefore, a consideration during the design phase is determining which data can potentially be collected based on the underlying technology and how new data can be effectively integrated. In contrast, refs. [24; 36] propose methodologies like dataset updates and a Sketch algorithm, respectively, for real-time data aggregation and processing within DTN. The work [26] employs that sketch algorithm to enhance data collection efficiency, illustrating practical implementations in DTN environments.  
Beyond acquisition, the type of data collected is critical and can include end-to-end information, configuration data, link quality metrics, buffer utilization, and more, depending on network management goals. Storing these data in a repository demands thoughtful consideration regarding scalability and data retention strategies. For instance, adopting the TICK stack (Telegraf, InfluxDB, Chronograf, and Kapacitor), as in [37], ensures efficient time-series data management, supporting both real-time analytics and historical data preservation.  
Figure 3: Overview of data acquisition and storage tools for Digital Twin Networks.  
In addition to the TICK stack, other scalable data storage solutions, as shown in Figure 3, like the Hadoop Distributed File System, Apache Spark, and Prometheus, are viable options, available in [38; 39; 40]. These platforms offer robust capabilities for managing large volumes of data in distributed environments, catering to the diverse needs of DTNs. Certainly, the selection of a repository depends on specific needs and objectives. For instance, in certain scenarios, simple file storage may suffice.  
Lastly, effective data transformation and aggregation techniques are essential to enhance the utility of collected data for network modeling and decision-making processes.  
Lightweight data collection methods are preferred to minimize resource overhead on network equipment while ensuring data relevance and accuracy for model development.