]> China Mobile

Beijing China limeiyjy@chinamobile.com

China Mobile

Beijing China zhouchengyjy@chinamobile.com

China Mobile

Beijing China chendanyang@chinamobile.com

Huawei

bill.wu@huawei.com

Huawei

yangyuanyuan55@huawei.com

IRTF Network Management network digital twin data generation data optimization Network Digital Twin (NDT) can be used as a secure and cost-effective environment for network operators to evaluate network performance in various what-if scenarios. Recently, Artificial Intelligence (AI) models, especially neural networks, have been applied for NDT modeling. The quality of AI models mainly depends on two aspects: model architecture and data. This memo focuses on the impact of how to improve the model quality from the data perspective.

Introduction Digital twin is a virtual representation of a physical system (twin) that is continually updated with the physical system's performance, maintenance, and health status data throughout the physical system's life cycle. Network Digital Twin (NDT) is a digital twin that is used in the context of networking, as defined in . NDT can be used as a secure and cost-effective environment for network operators to evaluate network performance and configurations in various what-if scenarios. NDT is applicable to various types of networks, such as wireless networks, optical networks, data center networks, Internet of Things (IoT) networks, and vehicular networks. Artificial Intelligence (AI) models, particularly neural networks (NNs), have been applied to modeling complex network environments for various applications, including performance evaluation, traffic prediction, resource allocation, and service self-healing. AI-driven network modeling facilitates the creation of real-time, lightweight, and highly accurate NDT. The quality of AI models mainly depends on two aspects: model architecture and training data quality. The role of data has recently been highlighted by the emerging concept of data-centric AI . This memo focuses on the impact of training data on the model. The quality of training data can affect the accuracy and generalization ability of the model. This memo focuses on how to design data generation and optimization methods for NDT modeling, which can generate simulated network data to address the limitation of insufficient practical data and select high-quality data from various data sources. Using high-quality data for training can improve the accuracy and generalization ability of the model. This document does not define a specific data generation algorithm or optimization framework. Instead, it discusses potential approaches and considerations for generating and selecting data for NDT modeling.

Acronyms and Abbreviations NDT: Network Digital Twin AI: Artificial Intelligence AIGC: AI-Generated Content ToS: Type of Service OOD: Out-of-Distribution FIFO: First In First Out SP: Strict Priority WFQ: Weighted Fair Queuing DRR: Deficit Round Robin BFS: Breadth-First Search CBR: Constant Bit Rate

Requirements The modeling performance is vital in NDT, which is involved in typical network management scenarios such as planning, construction, operation, and optimization. Recently, some studies have applied AI models to NDT modeling, such as RouteNet , MimicNet and m3 . AI is a data-driven technology whose performance heavily depends on data quality. Data-centric AI shifts the focus from model architecture to improving data quality and management through various techniques such as data augmentation, self-supervision, data cleaning, data selection, and data privacy. For example, data augmentation can create additional augmented samples. Self-supervised models can be developed without the need for manual labels or features. Data selection methods can help identify the most valuable samples. In many cases, network data sources are diverse and of varying quality, making it difficult to directly serve as training data for NDT AI models:

• Practical data from production networks: Data from production networks usually have high value, but the availability, coverage, and accessibility are limited. Moreover, it is not practical in production networks to collect data under various configurations;
• Network simulators: Network simulators (e.g., NS-3 and OMNeT++) can be used to generate simulated network data, which can address the limitations of data quantity and scenario diversity to some extent. However, simulation is usually time-consuming. In addition, there are usually differences between simulated data and practical data from production networks, which hinders the application of trained models to production networks;
• Generative AI models: With the development of AI-Generated Content (AIGC) technology, generative AI models can be used to generate simulated network data, which can solve the problems of quantity and diversity to a certain extent. However, the generated data needs to be further validated to ensure consistency with production network data.

Therefore, data generation and optimization methods for NDT modeling are needed, which can generate simulated network data to solve the problem of practical data shortage and select high-quality data from multi-source data. High-quality data meets the requirements of high accuracy, diversity, and fitting the actual situation of practical data. Training with high-quality data can improve the accuracy and generalization of NDT performance models.

Framework of Data Generation and Optimization The framework of data generation and optimization for NDT modeling is shown in Figure 1, which includes three stages: the data generation stage, the data optimization stage, and the data assessment stage.

Framework of Data Generation and Optimization for NDT | | | +-------+ | Sim. | | | v | | | +-------v-------+ | | |Routing| | | | | Candidate | | | | Netw. Constr. | | | |policy +-> +-+--+--> data | | | | Verification | | | +-------+ | | | | | | | | +---------------+ | | | GenAI | | | v | | | | | | +-------+ | Model | | | +------+-------+-+ | | +-------v-------+ | | |Traffic| | | | | | Data Selection | | | | DownsTask | | | |matrix | +-------+ | | | | <--| | Verification | | | +-------+ Data | | | - Easy samples | | | +---------------+ | | Network generator | | | - Hard samples | | | | | | config. | | | - OOD samples | | | v | | | | +----------------+ | | High-quality data | +-----------^---------+ +--------------------+ +---------+---------+ | | +-----------+----------------------------------------------v---------+ | Data Repository of NDT | +--------------------------------------------------------------------+ ]]>

Data Generation Stage The data generation stage aims to generate candidate data (simulated network data) to address the limitation of insufficient practical data from production networks. This stage first generates network configurations and then imports them into data generators to generate the candidate data.

• Network configurations: Network configurations typically include network topology, routing policy, and traffic matrix. These configurations need to be diverse to cover a wide range of network scenarios. Topology configurations include the number and structure of nodes and edges, node buffers' size and scheduling strategy, link capacity, etc. Routing policy determines the path of a packet from the source to the destination. The traffic matrix describes the traffic entering and leaving the network, including the traffic source, destination, time, packet size distribution, and Type of Service (ToS).
• Data generators: Data generators can be network simulators (e.g., NS-3 and OMNeT++) and/or the generative AI models (e.g., GPT and LLaMA). Network configurations are imported into data generators to generate candidate data.

Data Optimization Stage The data optimization stage aims to optimize the candidate data from various sources to select high-quality data, which is verified through the data quality assessment stage.

• Candidate data: Candidate data includes simulated network data generated in the data generation stage and data collected from production networks.
• Data selection: The data selection module analyzes the candidate data to filter out the easy, hard, and Out-of-Distribution (OOD) samples. Hard examples refer to samples that are difficult for the model to accurately predict. During the training process, including hard examples can improve model robustness for challenging samples. Then the easy samples and hard samples are considered valid samples and added to the training data. OOD samples are considered invalid and removed.

Data Assessment Stage The data assessment stage aims to verify, from multiple perspectives, whether the data produced by the data optimization stage meets the quality requirements of NDT modeling, and to provide feedback for further data optimization. It mainly includes sub-modules of statistical & distribution verification, network constraint verification，and downstream task performance verification. The result of this verification is fed back to the data selection module of the data optimization stage , forming a closed loop that iteratively improves both the data optimization strategy and the resulting data quality. Data that passes the above verifications is regarded as high-quality data and is stored in the Data Repository of NDT for use in NDT model training and other applications. Data quality dimensions and assessment mechanisms are further detailed in Section 7.

Data Generation

Network Configuration This section describes how to generate network configurations, including network topology, routing policy, and traffic matrix. Then these configurations will be imported into data generators to generate the candidate data.

Network Topology Network topologies are generated using the Power-Law Out-Degree algorithm, where parameters are set according to real-world topologies in the Internet Topology Zoo. When the flow rate exceeds the link bandwidth or the bandwidth set for the flow, the packet is temporarily stored in the node buffe r. A larger node buffer size may introduce larger delay and reduce packet loss probability. The node scheduling policy determines the time and order of packet transmission, which is randomly selected from the policies such as First In First Out (FIFO), Strict Priority (SP), Weighted Fair Queuing (WFQ), and Deficit Round Robin (DRR). A larger link capacity can reduce delay and congestion under the same traffic load. To cover diverse link loads to get good coverage of possible scenarios, we set the link capacity to be proportional to the total average bandwidth of the flows passing through the link.

Routing Policy Routing policy plays a crucial role in routing protocols, which determines the path of a packet from the source to the destination.

• Default: We set the weight of all links in the topology to be the same, that is, equal to 1. Then we use the Dijkstra algorithm to generate the shortest path configuration. Dijkstra algorithm iteratively selects the node with the minimum tentative distance and updates the distances of its adjacent nodes to calculate the shortest path in a weighted graph.
• Variants: We randomly select some links (the same link can be chosen more than once) and add a small weight to them. Then we use the Dijkstra algorithm to generate a series of variants of the default shortest path configuration based on the weighted graph. These variants can add some randomness to the routing configuration to cover longer paths and larger delays.

Traffic Matrix The traffic matrix is very important for network modeling. The traffic matrix can be seen as a traffic demand description, which describes the traffic entering/leaving the network, including the source, destination, distribution of the traffic, etc. We generate traffic matrix configurations with variable traffic intensity to cover low to high loads. The parameters packet sizes, packet size probabilities, and ToS are generated according to the validation dataset analysis to have similar distributions. The arrival of packets for each source-destination pair is modeled using one of the time distributions such as Poisson, Constant Bit Rate (CBR), and ON-OFF.

Data Generator

Network Simulator Network simulators make distinct trade-offs among fidelity, speed, and scale, and can be broadly classified into three categories:

• Packet-level simulation explicitly models the generation, transmission, and processing of individual packets. This approach achieves high fidelity and enables detailed observation of microscopic behaviors—such as protocol dynamics (e.g., TCP congestion control) and per-packet queueing effects. It is commonly used for evaluating mechanisms such as new transport protocols and latency-sensitive applications. However, the high computational cost typically limits its use to small- or moderate-scale network scenarios. Representative tools include ns-3 and OMNeT++.
• Flow-level simulation abstracts traffic into continuous flows, described by aggregate parameters such as average rate. This method achieves high scalability and is well-suited for macroscopic studies—such as Internet-wide traffic engineering, routing convergence, and capacity planning. While it sacrifices packet-level details (e.g., burst-induced queueing behavior), it remains effective for analyzing large-scale network properties where fine-grained fidelity is not critical. Typical implementations include flowSim and ASTRA-sim .
• Analytical modeling, often grounded in queueing theory, employs mathematical formulations to represent network behavior. This approach offers the highest computational efficiency and is particularly useful for deriving performance bounds and understanding fundamental trade-offs among system parameters. It is commonly applied to analyze idealized network elements, such as a single buffered link. A key limitation, however, is its reliance on simplifying assumptions, which may not capture the full complexity and variability of real-world traffic.

Generative AI Model Generative AI (GenAI) can be used to generate synthetic network data. By learning patterns from existing network traces, generative models—such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models—can produce realistic, high-dimensional network traffic data. This capability is particularly valuable in scenarios where real data is scarce, sensitive, or difficult to obtain. For instance, GenAI can be used to generate synthetic packet traces that preserve the statistical properties and temporal dependencies of real traffic without exposing private information, which may facilitate data sharing for research. It can also model rare but critical events, such as network attacks or flash crowds, to augment datasets for robustly training intrusion detection systems or evaluating protocol resilience under stress. A key consideration, however, is the fidelity and representativeness of the generated data, which hinges on the quality of the training data and the model's ability to capture the full breadth of network stochasticity, and the generated data may contain biases or unrealistic patterns.

Data Optimization This section describes how to optimize the data from various sources to select high-quality data, which includes the seed sample selection phase and incremental optimization phase. Candidate data includes simulated network data generated in the data generation stage and real data from production networks. Data optimization supports a variety of selection strategies, including high fidelity, high coverage, etc. High fidelity means that the selected data can have characteristics similar to production network data (e.g., having similar topologies, routing policies, traffic models, etc.), and high coverage means that the selected data can cover as many scenarios as possible.

Seed Sample Selection Phase In the seed sample selection phase, seed samples are selected through the following steps to provide high-quality initial samples for the incremental optimization phase. STEP 1: Training feature extraction model and feature extraction. (1.1) The training data D' is selected from the candidate data D according to the selection strategy. For the high fidelity strategy, production network data is used as the training data D'; for the high coverage strategy, the real data and simulated data are used together as the training data D'. (1.2) Feature extraction model E is trained using the training data D'. Feature extraction model E is a network performance evaluation model that can be used to estimate performance indicators such as delay, jitter and packet loss (such as RouteNet). (1.3) Use the feature extraction model E obtained in STEP (1.2) to extract the features of the training data D' obtained in STEP (1.1). A network can be defined as a set of flow F, queue Q, and link L. The link state SF (such as link utilization), queue state SQ (such as port occupancy), and flow state SL (such as delay, throughput, packet loss, etc.) are taken as features. Each sample in the training data D' is converted to a feature vector [SF,SQ,SL]. STEP 2: Clustering. Cluster the training data D' after feature extraction. Clustering (such as K-means and DBSCAN) is an unsupervised machine learning technique that can identify groups in the data based on feature similarity, divide the data into multiple clusters, and the samples in the same cluster have similarities. Repeat STEP 3 and STEP 4 until all clusters have been traversed. STEP 3: Calculating cluster centers and nearest neighbors. (3.1) Calculate cluster centers. The method of calculating cluster centers is determined according to the clustering algorithm used in STEP 2. For example, using K-means clustering algorithm, the cluster center is calculated by finding the average of all data points in the cluster. These cluster centers are added to the seed dataset DS. (3.2) Calculate k nearest neighbors of each cluster center and add them to the seed dataset DS. Suitable nearest neighbor calculation methods can be used, such as Euclidean distance, cosine distance, etc. STEP 4: Expert knowledge verification. (4.1) Expert knowledge can be used to verify the validity of samples through the range of indicators such as delay, queue occupation, and link utilization. If the verification passes, go to STEP 3. Otherwise, go to STEP (4.2). (4.2) Randomly select m samples from the seed dataset DS and remove them. Calculate the nearest neighbors of the removed m samples, add them to the seed data set DS, and go to STEP (4.1).

Incremental Optimization Phase The seed samples are taken as the initial training dataset. The filter model analyzes the remaining candidate samples to filter out the easy, hard and OOD samples. Then the easy samples and hard samples are added to the training dataset. These processes are repeated to iteratively optimize the filter model and the training data until the high-quality data meets the constraints.

• Easy samples: Easy samples are data points where the model's predictions align closely with the true labels, often with high confidence. While training on easy samples can lead to good performance on familiar data, relying solely on them may limit the model's ability to handle complex or ambiguous cases, potentially causing overfitting and poor generalization to unseen data.
• Hard samples: Hard samples are data points where the model struggles, producing inaccurate, ambiguous, or low-confidence predictions. These samples can help improve model robustness and generalization, as they expose weaknesses and encourage learning more discriminative features. Techniques like Online Hard Example Mining (OHEM), contrastive learning (focusing on hard negatives), and curriculum learning (gradually introducing harder samples) leverage hard samples to enhance model performance, prevent overfitting, and identify potential data issues such as labeling errors or biases.
• OOD samples: OOD samples refer to data points that significantly deviate from the training distribution, which may need to be detected removed depending on the training objective. Common detection methods include uncertainty estimation (e.g., Bayesian neural networks), density-based approaches (e.g., VAEs), distance-based metrics (e.g., Mahalanobis distance), outlier exposure, and energy-based models.

Data Assessment This section defines the data quality dimensions and assessment mechanisms used in the data assessment stage described in Section 4.3, in order to systematically verify whether the data produced by data generation and optimization meets the quality requirements of NDT modeling.

Data Quality Dimension The quality of data for NDT modeling can be evaluated along the following dimensions:

• Accuracy: The degree to which the data reflects the actual physical network states or expected network behaviors. Accuracy can be assessed by comparing the optimized data against measured data using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), or distribution distance measures.
• Completeness: The degree of coverage of the data across the relevant time, space (e.g., network nodes, links), and feature dimensions. Completeness can be assessed using metrics such as the missing rate of fields or time-series sampling points, and sample coverage rate.
• Consistency: The logical consistency of the data describing the same network entity, across different data sources, time points, or after different processing steps. Consistency can be assessed by comparing statistical values of the same metric obtained from different sources, or by performing logical and temporal verification.
• Timeliness: The delay between data generation/collection and its availability for NDT modeling, which should meet requirements of the target NDT application. Timeliness can be assessed using metrics such as the delay between the data collection (or generation) timestamp and the availability timestamp, and the data update frequency.
• Diversity: The degree to which the data covers different network scenarios, configurations, traffic patterns, and rare events such as faults and attacks. Diversity can be assessed using metrics such as feature space coverage, category distribution statistics, or entropy-based diversity measures.
• Task adaptability: The degree to which the data is suitable for, and improves the performance of, models developed for specific NDT tasks (e.g., performance prediction, fault diagnosis, decision-making). Task adaptability can be assessed using task-specific performance metrics (e.g., accuracy, F1-score, AUC, prediction error) obtained by training and/or validating models with the data.

Data Quality Mechanisms Data quality assessment can incorporate a hybrid approach combining statistical verification, network constraint validation, and downstream task evaluation, corresponding to the steps illustrated in the data assessment stage of Figure 1.

• Statistical and distribution verification: This step compares statistical and distributional properties of the optimized data against real network data, mainly addressing the accuracy and consistency. Typical methods include Q-Q plots, the Kolmogorov-Smirnov (KS) test, and other distribution-distance measures (e.g., Wasserstein distance).
• Network constraint verification: This step verifies whether the optimized data conforms to protocol behaviors and network operational constraints (e.g., valid ranges of delay, queue occupation, and link utilization), to identify data that, although statistically plausible, does not satisfy network constraints, mainly addressing the accuracy and completeness dimensions.
• Downstream task performance verification: This step evaluates the data by training and/or validating models for the target NDT task and measuring task-specific performance metrics, mainly addressing the task adaptability dimension. The result of this verification is fed back to the data selection module of the data optimization stage, forming a closed loop that iteratively improves both the data optimization strategy and the resulting data quality.

Use Cases NDT can be applied to various types of networks, including data center networks, IP bearer networks, vehicular networks, wireless networks, optical networks, and IoT networks. This section describes several examples where data generation and optimization can support NDT modeling.

Configuration Evaluation and Optimization in Data Center Networks Data centers support a wide range of Internet services, consisting of numerous computing and storage nodes linked by a data center network (DCN), which serves as the communication backbone. The DCN faces challenges related to its large scale, diverse applications, high power density, and the need for reliability. NDT can evaluate configurations and technologies to reduce the risk of failures. For NDT to be effective, it must accurately model DCN traffic. A key challenge lies in generating realistic network traffic. By analyzing traffic patterns, data generation and optimization techniques can assist in creating simulated network data and selecting suitable data from both production and simulated sources. Numerous factors, such as the type of business, network size, volume of traffic, and load, influence traffic patterns in extensive DCNs. Moreover, these traffic patterns are dynamic and evolve over time. For instance, workloads that are sensitive to latency, like online transaction processing, tend to peak during the day, whereas workloads for online analytical processing are more prevalent at night.

Performance Prediction in IP Bearer Networks Internet service providers encounter challenges in delivering high-bandwidth, low-latency, and reliable services, especially in large networks like metropolitan area networks (MANs) . The widely adopted IP protocol adheres to a best-effort principle, making predictable performance difficult and complicating the stability and availability of network services during failures. NDT can function as a high-fidelity simulation platform for predicting IP bearer network performance. Accurate network status information is vital for optimizing protocols and identifying faults. Recent advancements in in-band network telemetry (INT) technology have allowed the integration of network performance data into packet headers on the data plane. Utilizing performance data collected through INT, data generation and optimization techniques can create additional simulated data and select suitable training data for NDT modeling. ## Task Offloading in Vehicular Networks The rise of vehicular networks has facilitated various delay-sensitive applications, including autonomous driving and navigation. However, vehicles with limited resources struggle to meet the low/ultra-low latency requirements. To address this, computationally intensive tasks can be offloaded to resource-rich platforms like nearby vehicles, edge servers, and cloud servers. The dynamic nature of these networks, along with strict low-delay demands and large task data, presents significant offloading challenges. NDT can provide a virtual environment for monitoring and evaluating vehicular networks, aiding in effective offload decisions. Additionally, machine learning algorithms are increasingly utilized for task offloading to enhance accuracy and efficiency. Unlike traditional communication networks, vehicular networks are more dynamic and heterogeneous, leading to data shortages and quality issues. Data generation and optimization techniques can simulate data for adaptability and filter high-quality data from various sources, thereby improving model training effectiveness.

Discussion Several topics related to data generation and optimization for NDT performance modeling require further discussion.

• Data generation methods: 1) Generate configurations that cover representative scenarios and support different network scales. 2) Select data generators considering trade-offs among accuracy, speed, fidelity, etc. 3) Use data augmentation technology to expand the training data by using a small amount of practical data to generate similar data through prior knowledge.
• Data optimization methods: 1) Select data from multi-source candidate data, including hard sample mining, OOD detection, etc. 2) Verify whether the data quality meets the requirements.
• Deployment: Time/space complexity and explainability of the data generation and optimization methods.
• Directions on standardization: Which aspects of data generation and optimization for NDT modeling may require further standardization in IETF? # Security Considerations

TBD

IANA Considerations This document has no IANA actions.

Informative References ACM Computing Surveys IEEE/ACM Transactions on Networking ACM SIGCOMM 2021 Conference ACM SIGCOMM 2024 Conference IEEE INFOCOM IEEE ISPASS China Mobile China Mobile China Mobile Huawei Orange Orange The application of Digital Twin technology in the networking field is meant to develop various rich network applications, realize efficient and cost-effective data-driven network management, and accelerate network innovation. This document presents an overview of the concept of Network Digital Twin (NDT), provides the basic definitions and a reference architecture, lists a set of application scenarios, and discusses such technology's benefits and key challenges. This document is a product of the Network Management Research Group (NMRG) of the Internet Research Task Force (IRTF). This document reflects the consensus of the research group. It is not a candidate for any level of Internet Standard and is published for informational purposes.

Acknowledgments TODO acknowledge.