Data Generation and Optimization for Network Digital Twin
draft-li-nmrg-dtn-data-generation-optimization-05

Network Management                                                 M. Li
Internet-Draft                                                   C. Zhou
Intended status: Informational                                   D. Chen
Expires: 3 January 2027                                     China Mobile
                                                                   Q. Wu
                                                                 Y. Yang
                                                                  Huawei
                                                             2 July 2026

       Data Generation and Optimization for Network Digital Twin
           draft-li-nmrg-dtn-data-generation-optimization-05

Abstract

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

Status of This Memo

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Acronyms and Abbreviations  . . . . . . . . . . . . . . . . .   3
   3.  Requirements  . . . . . . . . . . . . . . . . . . . . . . . .   4
   4.  Framework of Data Generation and Optimization . . . . . . . .   5
     4.1.  Data Generation Stage . . . . . . . . . . . . . . . . . .   6
     4.2.  Data Optimization Stage . . . . . . . . . . . . . . . . .   6
     4.3.  Data Assessment Stage . . . . . . . . . . . . . . . . . .   7
   5.  Data Generation . . . . . . . . . . . . . . . . . . . . . . .   7
     5.1.  Network Configuration . . . . . . . . . . . . . . . . . .   7
       5.1.1.  Network Topology  . . . . . . . . . . . . . . . . . .   7
       5.1.2.  Routing Policy  . . . . . . . . . . . . . . . . . . .   8
       5.1.3.  Traffic Matrix  . . . . . . . . . . . . . . . . . . .   8
     5.2.  Data Generator  . . . . . . . . . . . . . . . . . . . . .   8
       5.2.1.  Network Simulator . . . . . . . . . . . . . . . . . .   8
       5.2.2.  Generative AI Model . . . . . . . . . . . . . . . . .   9
   6.  Data Optimization . . . . . . . . . . . . . . . . . . . . . .  10
     6.1.  Seed Sample Selection Phase . . . . . . . . . . . . . . .  10
     6.2.  Incremental Optimization Phase  . . . . . . . . . . . . .  11
   7.  Data Assessment . . . . . . . . . . . . . . . . . . . . . . .  12
     7.1.  Data Quality Dimension  . . . . . . . . . . . . . . . . .  12
     7.2.  Data Quality Mechanisms . . . . . . . . . . . . . . . . .  13
   8.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .  14
     8.1.  Configuration Evaluation and Optimization in Data Center
           Networks  . . . . . . . . . . . . . . . . . . . . . . . .  14
     8.2.  Performance Prediction in IP Bearer Networks  . . . . . .  14
     8.3.  Task Offloading in Vehicular Networks . . . . . . . . . .  15
   9.  Discussion  . . . . . . . . . . . . . . . . . . . . . . . . .  15
   10. Security Considerations . . . . . . . . . . . . . . . . . . .  16
   11. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  16
   12. Informative References  . . . . . . . . . . . . . . . . . . .  16
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .  16
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  16

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1.  Introduction

   Digital twin is a virtual instance 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
   [I-D.irtf-nmrg-network-digital-twin-arch].  NDT can be used as a
   secure and cost-effective environment for network operators to
   evaluate network 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 proven to be highly effective in 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 data.  The role of data has recently been
   highlighted by the emerging concept of data-centric AI
   [Data-Centric-AI].  This memo focuses on the impact of training data
   on the model.  The quality of training data will directly 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 solve the
   problem of practical data shortage 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.

2.  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

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   WFQ: Weighted Fair Queuing

   DRR: Deficit Round Robin

   BFS: Breadth-First Search

   CBR: Constant Bit Rate

3.  Requirements

   The modeling performance is vital in NDT, which is involved in
   typical network management scenarios such as planning, construction,
   operation, optimization, and operation.  Recently, some studies have
   applied AI models to NDT modeling, such as RouteNet [RouteNet],
   MimicNet [MimicNet] and m3 [m3].  AI is a data-driven technology
   whose performance heavily depends on data quality.

   Data-centric AI [Data-Centric-AI] shifts the focus from model
   architecture to improving data 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 quantity, type, and
      accuracy 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 solve
      the problems of quantity, diversity, and accuracy to a certain
      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;

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   *  Generative AI models: With the development of AI-Generated Content
      (AIGC) technology, generative AI models (e.g., GPT and LLaMA) can
      be used to generate simulated network data, which can solve the
      problems of quantity and diversity to a certain extent.  However,
      the accuracy of the data generated by generative AI models is
      limited and often has gaps with practical data from production
      networks.

   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.

4.  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.

       Data Generation       Data Optimization       Data Assessment
  +---------------------+  +--------------------+  +-------------------+
  |                     |  |                    |  |                   |
  | +-------+           |  |  +-----------+     |  | +---------------+ |
  | |Network|           |  |  | Real Netw.|     |  | | Stat & Distr. | |
  | | Topo  | +-------+ |  |  |   data    |     |  | | Verification  | |
  | +-------+ |       | |  |  +-----+-----+     |  | +---------------+ |
  |           | Netw. | |  |        |       +---+-->         |         |
  | +-------+ | 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 (remove) | |  |         v         |
  |                     |  | +----------------+ |  | High-quality data |
  +-----------^---------+  +--------------------+  +---------+---------+
              |                                              |
  +-----------+----------------------------------------------v---------+
  |                         Data Repository of NDT                     |
  +--------------------------------------------------------------------+

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     Figure 1: Framework of Data Generation and Optimization for NDT

4.1.  Data Generation Stage

   The data generation stage aims to generate candidate data (simulated
   network data) to solve the problem of the shortage of 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 as many scenarios as
      possible.  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 taking from the source to the destination.  The
      traffic matrix describes the traffic entering/leaving the network,
      and leaving the footprint in the paths of the network which
      includes the traffic's source, destination, time and packet size
      distribution, Type of Service (ToS), etc.

   *  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.

4.2.  Data Optimization Stage

   The data optimization stage aims to optimize the candidate data from
   various sources to select candidate 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 the practical data from
      production networks.

   *  Data selection: The data selection module investigates 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, exposing the model to more hard examples will
      enable it to perform better on such samples later on.  Then the
      easy samples and hard samples are considered valid samples and
      added to the training data.  OOD samples are considered invalid
      and removed.

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4.3.  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 to the
   data optimization stage to support continuous improvement.  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.

5.  Data Generation

5.1.  Network Configuration

   This section will describe 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.

5.1.1.  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 buffer.  A
   larger node buffer size means a larger delay and possibly a lower
   packet loss rate.  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 means a smaller delay and less congestion.  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.

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5.1.2.  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 uses
      Breadth-First Search (BFS) to find the single source shortest path
      in a weighted digraph.

   *  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.

5.1.3.  Traffic Matrix

   The traffic matrix is very important for network modeling.  The
   traffic matrix can be seen as a network map, 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.

5.2.  Data Generator

5.2.1.  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 widely

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      considered the reference method for evaluating mechanisms like 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 [flowSim] and ASTRA-sim
      [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.

5.2.2.  Generative AI Model

   Generative AI (GenAI) presents a novel paradigm for synthesizing
   network data.  By learning the underlying distributions and complex
   temporal dynamics 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, thus facilitating 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, avoiding the introduction of subtle biases or
   unrealistic artifacts.

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6.  Data Optimization

   This section describes how to optimize the data from various sources
   to filter out 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 fit the real 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.

6.1.  Seed Sample Selection Phase

   In the seed sample selection phase, high-quality 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,
   the real 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 evaluate 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 feature 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 occupation), 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 automatically discover the natural groups in the
   data, divide the data into multiple clusters, and the samples in the
   same cluster have similarities.

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   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 passed, 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).

6.2.  Incremental Optimization Phase

   The seed samples are taken as the initial training dataset.  The
   filter model investigates 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 are crucial for improving model
      robustness and generalization, as they expose weaknesses and
      encourage learning more discriminative features.  Techniques like
      Online Hard Example Mining (OHEM), contrastive learning (focusing

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      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 should be detected
      and removed.  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.

7.  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.

7.1.  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 behavioural patterns.
      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.

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   *  Timeliness: The delay between data generation/collection and its
      availability for NDT modeling, which should meet the (near-)real-
      time 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.

7.2.  Data Quality Mechanisms

   Data quality assessment can incorporate a hybrid approach combining
   mathematical verification, protocol 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 dimensions.  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), so as to filter out data that,
      although statistically plausible, is not physically valid, mainly
      addressing the accuracy and completeness dimensions.

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   *  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 and timeliness 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.

8.  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
   highlights the significance of data generation and optimization in
   NDT by presenting several typical use cases.

8.1.  Configuration Evaluation and Optimization in Data Center Networks

   Data centers are essential for the growth 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 optimize both real and simulated data.  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.

8.2.  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

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   in-band network telemetry (INT) technology have allowed the
   integration of network performance data into packet headers on the
   data plane.  Utilizing real performance data from INT, data
   generation and optimization techniques can create fine-grained
   simulated data, enhancing both real and simulated datasets for better
   model training outcomes.

8.3.  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 is an emerging
   method that allows real-time monitoring of 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.

9.  Discussion

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

   *  Data generation methods: 1) Generate configurations that cover
      enough scenarios and scale from small to large networks. 2) Choose
      data generators that consider 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 related research is suitable
      for promoting standards in IETF?

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10.  Security Considerations

   TBD

11.  IANA Considerations

   This document has no IANA actions.

12.  Informative References

   [ASTRA-sim]
              IEEE ISPASS, "Astra-sim2. 0: Modeling hierarchical
              networks and disaggregated systems for large-model
              training at scale", 2023.

   [Data-Centric-AI]
              ACM Computing Surveys, "Data-centric Artificial
              Intelligence: A Survey", 2025.

   [flowSim]  IEEE INFOCOM, "Bandwidth sharing: objectives and
              algorithms", 1999.

   [I-D.irtf-nmrg-network-digital-twin-arch]
              Zhou, C., Yang, H., Duan, X., Lopez, D., Pastor, A., Wu,
              Q., Boucadair, M., and C. Jacquenet, "Network Digital Twin
              (NDT): Concepts and Reference Architecture", Work in
              Progress, Internet-Draft, draft-irtf-nmrg-network-digital-
              twin-arch-13, 1 July 2026,
              <https://datatracker.ietf.org/doc/html/draft-irtf-nmrg-
              network-digital-twin-arch-13>.

   [m3]       ACM SIGCOMM 2024 Conference, "m3: Accurate Flow-Level
              Performance Estimation using Machine Learning", 2024.

   [MimicNet] ACM SIGCOMM 2021 Conference, "MimicNet: Fast Performance
              Estimates for Data Center Networks with Machine Learning",
              2021.

   [RouteNet] IEEE/ACM Transactions on Networking, "RouteNet-Fermi:
              Network Modeling With Graph Neural Networks", 2023.

Acknowledgments

   TODO acknowledge.

Authors' Addresses

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   Mei Li
   China Mobile
   Beijing
   China
   Email: limeiyjy@chinamobile.com

   Cheng Zhou
   China Mobile
   Beijing
   China
   Email: zhouchengyjy@chinamobile.com

   Danyang Chen
   China Mobile
   Beijing
   China
   Email: chendanyang@chinamobile.com

   Qin Wu
   Huawei
   Email: bill.wu@huawei.com

   Yuanyuan Yang
   Huawei
   Email: yangyuanyuan55@huawei.com

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