Digital Twin for Networking: A Data-Driven Performance Modeling Perspective

Emerging technologies and applications make the network unprecedentedly complex and heterogeneous, leading the network operations to be costly and risky. The digital twin network (DTN) can ease these burdens by virtually enabling users to understand how performance changes accordingly with modifications. For this “What-if” performance evaluation, conventional simulation and analytical approaches are inefficient, inaccurate, and inflexible, and we argue that data-driven methods are most promising. In this article, we identify three requirements (fidelity, efficiency, and flexibility) for performance evaluation. Then we present a comparison of selected data-driven methods and investigate their potential trends in data, models, and applications. We find that performance models have enabled extensive applications, while there are still significant conflicts between models' capacities to handle diversified inputs and limited data collected from the production network. We further illustrate the opportunities for data collection, model construction, and application prospects. This survey aims to provide a reference for performance evaluation while also facilitating future DTN research.

• Publication year

  2022

• Venue

  IEEE Network

• Publication date

  2022-06-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/MNET.119.2200080arXiv 2206.00310
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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