论文标题
生产感知工业网络交通建模的生成方法
A Generative Approach for Production-Aware Industrial Network Traffic Modeling
论文作者
论文摘要
工业4.0引起的新一波数字化浪潮呼吁无处不在且可靠的连通性，以执行和自动化工业运营。 5G网络可以提供异质垂直应用程序的极端要求，但是缺乏实际数据和现实的流量统计数据为工业环境的优化和配置带来了许多挑战。在本文中，我们研究了由德国特朗普工厂部署的激光切割机产生的网络流量数据。我们分析流量统计信息，捕获机器内部状态之间的依赖性，并将网络流量建模为生产状态依赖性随机过程。提出了两步模型，如下所示：首先，我们将生产过程建模为多状态半马尔可夫过程，然后我们学习了与生成模型的生产状态相关数据包间隔时间和数据包大小的条件分布。我们比较了各种生成模型的性能，包括变异自动编码器（VAE），条件变异自动编码器（CVAE）和生成对抗网络（GAN）。数值结果表明，取决于生产状态，交通统计数据的近似值很大。在所有生成模型中，CVAE总体上提供了最小的Kullback-Leibler Divergence的最佳性能。
The new wave of digitization induced by Industry 4.0 calls for ubiquitous and reliable connectivity to perform and automate industrial operations. 5G networks can afford the extreme requirements of heterogeneous vertical applications, but the lack of real data and realistic traffic statistics poses many challenges for the optimization and configuration of the network for industrial environments. In this paper, we investigate the network traffic data generated from a laser cutting machine deployed in a Trumpf factory in Germany. We analyze the traffic statistics, capture the dependencies between the internal states of the machine, and model the network traffic as a production state dependent stochastic process. The two-step model is proposed as follows: first, we model the production process as a multi-state semi-Markov process, then we learn the conditional distributions of the production state dependent packet interarrival time and packet size with generative models. We compare the performance of various generative models including variational autoencoder (VAE), conditional variational autoencoder (CVAE), and generative adversarial network (GAN). The numerical results show a good approximation of the traffic arrival statistics depending on the production state. Among all generative models, CVAE provides in general the best performance in terms of the smallest Kullback-Leibler divergence.
