Internet-Draft Data Generation and Optimization for DTN October 2023
Li, et al. Expires 21 April 2024 [Page]
Workgroup:
Internet Research Task Force
Internet-Draft:
draft-li-nmrg-dtn-data-generation-optimization-01
Published:
Intended Status:
Informational
Expires:
Authors:
M. Li
China Mobile
C. Zhou
China Mobile
D. Chen
China Mobile

Data Generation and Optimization for Digital Twin Network Performance Modeling

Abstract

Digital Twin Network (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 deep learning models mainly depends on two aspects: model architecture and data. This memo focuses on how to improve the model from the data perspective.

Requirements Language

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in RFC 2119 [RFC2119].

Status of This Memo

This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79.

Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet-Drafts is at https://datatracker.ietf.org/drafts/current/.

Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress."

This Internet-Draft will expire on 21 April 2024.

Table of Contents

1. Introduction

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 [I-D.irtf-nmrg-network-digital-twin-arch]. 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.

2. Acronyms & Abbreviations

DTN:
Digital Twin Network
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

3. Requirements

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 [RouteNet] and MimicNet [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:

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.

4. Framework of Data Generation and Optimization

The framework of data generation and optimization for DTN performance modeling is shown in Figure 1, which includes two stages: the data generation stage and the data optimization stage.

       Data generation                   Data optimization
+---------------------------+ +-------------------------------------+
|                           | |                                     |
| +---------+               | |              +---------+            |
| |         |               | | +----------+ |         |            |
| | Network |               | | | Practical| | Easy    |            |
| | topology| +-----------+ | | | data     | | samples |            |
| |         | |           | | | +-----+----+ |         |            |
| |         | | Network   | | |       |      |         | +--------+ |
| |         | | simulator | | | +-----v----+ |         | |        | |
| | Routing | |           | | | |          | | Hard    | | High   | |
| | policy  +->           +-+-+-> Candidate+-> samples +-> quality| |
| |         | |           | | | | data     | |         | | data   | |
| |         | | Generative| | | |          | |         | |        | |
| |         | | AI model  | | | +----------+ |         | +--------+ |
| | Traffic | |           | | |              | OOD     |            |
| | matrix  | +-----------+ | |              | samples |            |
| |         | Data generator| |              | (remove)|            |
| +---------+               | |              |         |            |
|  Network                  | |              +---------+            |
|  configuration            | |             Data selection          |
|                           | |                                     |
+---------------------------+ +-------------------------------------+
Figure 1: Framework of Data Generation and Optimization for DTN Performance Modeling

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 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.

4.2. Data Optimization Stage

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).

5. Data Generation

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. 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.

5.2. Routing Policy

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

5.3. Traffic Matrix

The traffic matrix is very important for network performance modeling. The traffic matrix can be regarded 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.

6. Discussion

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

7. Security Considerations

TBD

8. IANA Considerations

This document has no requests to IANA.

9. References

9.1. Informative References

[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, "Digital Twin Network: Concepts and Reference Architecture", Work in Progress, Internet-Draft, draft-irtf-nmrg-network-digital-twin-arch-03, , <https://datatracker.ietf.org/doc/html/draft-irtf-nmrg-network-digital-twin-arch-03>.
[MimicNet]
Zhang, Q. Zhang., NG, K. K.W. NG., Kazer, C. W. Kazer., Yan, S. Yan., Sedoc, J. Sedoc., and V. Liu. Liu, "MimicNet: Fast Performance Estimates for Data Center Networks with Machine Learning. In ACM SIGCOMM 2021 Conference (SIGCOMM ’21).", .
[RouteNet]
Rusek, K. Rusek., Suárez-Varela, J. Suárez-Varela., Almasan, P. Almasan., Barlet-Ros, P. Barlet-Ros., and A. Cabellos-Aparicio. Cabellos-Aparicio, "RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN. IEEE Journal on Selected Areas in Communication (JSAC), vol. 38, no. 10", .

9.2. Normative References

[RFC2119]
Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, , <https://www.rfc-editor.org/info/rfc2119>.

Authors' Addresses

Mei Li
China Mobile
Beijing
100053
China
Cheng Zhou
China Mobile
Beijing
100053
China
Danyang Chen
China Mobile
Beijing
100053
China