Data Generation and Optimization for Digital Twin Network Performance Modeling

Digital twin is a virtual instance of a physical system (twin) that is continually updated with the latter's performance, maintenance, and health status data throughout the physical system's life cycle. Digital Twin Network (DTN) is a digital twin that is used in the context of networking . DTN can be used as a secure and cost-effective environment for network operators to evaluate network performance in various what-if scenarios. Recently, AI models, especially neural networks, have been applied for DTN performance modeling. The quality of AI models mainly depends on two aspects: model architecture and data. 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 DTN performance 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.

Digital Twin Network Artificial Intelligence AI-Generated Content Type of Service (ToS) Out-of-Distribution

Performance modeling is vital in DTN, which is involved in typical network management scenarios such as planning, operation, optimization, and upgrade. Recently, some studies have applied AI models to DTN performance modeling, such as RouteNet and MimicNet . AI is a data-driven technology whose performance heavily depends on data quality. Network data sources are diverse and of varying quality, making it difficult to directly serve as training data for DTN performance 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; 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 DTN performance 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 DTN performance models.

The framework of data generation and optimization for DTN performance modeling is shown in , which includes two stages: the data generation stage and the data optimization 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 from the source to the destination. The traffic matrix describes the traffic entering/leaving 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.

The data optimization stage aims to optimize the candidate data from various sources to select high-quality data. 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. High-quality data: High-quality data needs to meet the requirements of high accuracy, diversity, and fitting the actual situation of practical data, which can be verified by expert knowledge (such as the ranges of delay, queue utilization, link utilization, and average port occupancy).

Several topics related to data generation and optimization for DTN 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: 1) Time/space complexity and explainability of the data generation and optimization methods. 2) Provide feedback for data collection to form a closed loop.

TBD

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