论文标题
用于网络的数字双胞胎：数据驱动的性能建模观点
Digital Twin for Networking: A Data-driven Performance Modeling Perspective
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Emerging technologies and applications make the network unprecedentedly complex and heterogeneous, leading physical network practices 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. Although extensive applications have been enabled, 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.
