Data-Driven Routing: A Typical Application of Digital Twin Network

Wei, Zhiyu; Wang, Songtao; Li, Dan; Gui, Fei; Hong, Sihong

doi:10.1109/dtpi52967.2021.9540073

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Various structural reference architectures have been proposed for the effective use of the DTN. However, the existing structures do not provide concrete insights into the overall data flow, including the process of providing data to the AI model within the DTN [2,[15][16][17][18][19]. Moreover, it is essential to understand the overall process of providing customized data for various scenarios within the DTN and the intelligent model for effective network management.…”

Section: Proposed Data-driven Dtn Data Pipeline Architecture For Netw...mentioning

confidence: 99%

“…In this context, accurate modeling of the real environment in virtual reality has a significant impact on the performance of the DTN. Accordingly, various researches [12][13][14] on modeling traffic flow for accurate simulation, standardization, and research on DTN architecture have been conducted together [2,[15][16][17][18][19]. However, the existing architectures are abstracted, making it difficult to confirm the specific data flows for the actual DTN implementation.…”

Section: Introductionmentioning

confidence: 99%

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Network Traffic Prediction Model in a Data-Driven Digital Twin Network Architecture

Shin,

Oh,

Isah

et al. 2023

Electronics

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The proliferation of immersive services, including virtual reality/augmented reality, holographic content, and the metaverse, has led to an increase in the complexity of communication networks, and consequently, the complexity of network management. Recently, digital twin network technology, which applies digital twin technology to the field of communication networks, has been predicted to be an effective means of managing complex modern networks. In this paper, a digital twin network data pipeline architecture is proposed that demonstrates an integrated structure for flow within the digital twin network and network modeling from a data perspective. In addition, a network traffic modeling technique using data feature extraction techniques is proposed to realize the digital twin network, which requires the use of massive streaming data. The proposed method utilizes the data generated in the OMNeT++ environment and verifies that the learning time is reduced by approximately 25% depending on the feature extraction interval, while the accuracy remains similar.

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Section: Proposed Data-driven Dtn Data Pipeline Architecture For Netw...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network Traffic Prediction Model in a Data-Driven Digital Twin Network Architecture

Shin,

Oh,

Isah

et al. 2023

Electronics

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“…Vakaruk et al [24] presented a DTN called SPIDER for security training in 5G industrial environments. Wei et al [25] discussed data-driven routing which is a typical network function under DTN framework and demonstrate the potential of DTN to solve traditional network problems.…”

Section: Digital Twin Networkmentioning

confidence: 99%

Deep Learning Based Traffic Prediction Method for Digital Twin Network

et al. 2023

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Network traffic prediction (NTP) can predict future traffic leveraging historical data, which serves as proactive methods for network resource planning, allocation, and management. Besides, NTP can also be applied for load generation in simulated and emulated as well as digital twin networks (DTNs). This paper focuses on accurately predicting background traffic matrix (TM) of typical local area network (LAN) for traffic synchronization in DTN. A survey is firstly conducted on DTN, conventional model, and deep learning based NTP methods. Then, as the major contribution, a linear feature enhanced convolutional long short-term memory (ConvLSTM) model based NTP method is proposed for LAN. An autoregressive unit is integrated into the ConvLSTM model to improve its linear prediction ability. In addition, this paper further optimizes the proposed model from both spatial and channel-wise dimensions. Particularly, a traffic pattern attention (TPA) block and a squeeze & excitation (SE) block are derived and added to the enhanced ConvLSTM (eConvLSTM) model. Comparative experiments demonstrate that the eConvLSTM model outperforms all the baselines. It can improve the prediction accuracy by reducing the mean square error (MSE) up to 10.6% for one-hop prediction and 16.8% for multi-hops prediction, compared to the legacy CovnLSTM model, with still satisfying the efficiency requirements. The further enhancement of the eConvLSTM model can additionally reduce the MSE about 2.1% for one-hop prediction and 4.2% for multi-hops prediction, with slightly degrading efficiency. The proposed eConvLSTM model based NTP method can play a vital role on DTN traffic synchronization.

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“…Data-driven routing is a modern approach to network routing where the data within the packets is taken into account for dispatching the packets as opposed to using the header information only. Reference [29] has designed a digital twin network architecture used for analysis and simulation of network traffic management decisions. This virtual model is constantly updated with real-time data and stores the entire history of the network up to the current time.…”

Section: Master Of Applied Sciences-mehrad Vaezi; Mcmaster University...mentioning

confidence: 99%

Digital Twin Placement for Minimum Application Request Delay With Data Age Targets

Vaezi

Noroozi

Todd

et al. 2023

IEEE Internet Things J.

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Digital Twins are softwarized mirrors of physical systems. They can represent their corresponding physical counterparts in real-world applications and reflect the behavior of the latter under different scenarios with decent accuracy. In this thesis, we consider the case where an application requests data from multiple digital twins, each representing a physical system. The digital twins are hosted on execution servers located between the application and the set of physical devices. Each digital twin has to be periodically updated by its physical system and uses a portion of the execution server's computing resource to refresh itself. Due to the scarcity of computation resources of the execution servers, in this thesis, we have tackled the problem of optimal digital twin placement onto a limited set of execution servers. We are aiming at minimizing the latency of the digital twins' responses to the application's requests while keeping the age of information of served data below a certain threshold. We first formulate the problem as an integer quadratic program (IQP) and then transform it into a semidefinite program (SDP).We prove that the problem is NP-complete and propose polynomial-time approximation algorithms that solve the problem with different trade-offs between the accommodation of the application's request latency and the achievement of data age targets.iii I would like to express my gratitude to my supervisors, Professors Dongmei Zhao, George Karakostas, and Terence D. Todd who guided me throughout my graduate studies. I am also grateful to Dr. Douglas Down for his valuable comments as part of the examining committee. Finally, I would also like to thank my friends and family for their love and support.

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Data-Driven Routing: A Typical Application of Digital Twin Network

Cited by 7 publications

References 4 publications

Network Traffic Prediction Model in a Data-Driven Digital Twin Network Architecture

Network Traffic Prediction Model in a Data-Driven Digital Twin Network Architecture

Deep Learning Based Traffic Prediction Method for Digital Twin Network

Digital Twin Placement for Minimum Application Request Delay With Data Age Targets

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