NetGen: A Network Digital Twin Generator

To explore service-level appropriate network configurations efficiently, we propose a processing pipeline that generates a digital twin given only a packet capture trace from the network. Our pipeline outputs an instance of a discrete-event simulator, which can then be easily customized to explore arbitrary what-if configuration scenarios for the network. The parameters of this simulator instance are optimized to fit measured KPIs in the real-life network, using a genetic algorithm operating on a mix of actual fitness from the simulator and surrogate fitness implemented by neural networks. We show that our surrogate model can both speed up the time of finding a good fit, by 17%, as well as improve the fit, by 1.8x, compared to random parameter selection. We also evaluate our approach in an experimental testbed using what-if predictions of Wi-Fi primary channel configurations for several application workloads, showing 153% improvement in collision probability and 110% improvement in throughput predictions over a random baseline. Furthermore, we show that we only have to train our neural networks on a subset of the network workloads as it generalizes well.