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This paper introduces RouteNet-Gauss, a hybrid approach combining a hardware testbed with a machine learning model to accelerate and improve the accuracy of network simulation. The testbed acts as a hardware accelerator to generate training data, enabling the ML model to learn network behavior with high fidelity. Results demonstrate a 95% reduction in prediction error and a 488x speedup in inference time compared to DES methods, while also generalizing to larger and unseen network topologies.
RouteNet-Gauss slashes network simulation time by 488x while boosting accuracy, by using a hardware testbed to turbocharge ML model training.
Network simulation is pivotal in network modeling, assisting with tasks ranging from capacity planning to performance estimation. Traditional approaches such as Discrete Event Simulation (DES) face limitations in terms of computational cost and accuracy. This paper introduces RouteNet-Gauss, a novel integration of a testbed network with a Machine Learning (ML) model to address these challenges. By using the testbed as a hardware accelerator, RouteNet-Gauss generates training datasets rapidly and simulates network scenarios with high fidelity to real-world conditions. Experimental results show that RouteNet-Gauss significantly reduces prediction errors by up to 95% and achieves a 488x speedup in inference time compared to state-of-the-art DES-based methods. RouteNet-Gauss's modular architecture is dynamically constructed based on the specific characteristics of the network scenario, such as topology and routing. This enables it to understand and generalize to different network configurations beyond those seen during training, including networks up to 10x larger. Additionally, it supports Temporal Aggregated Performance Estimation (TAPE), providing configurable temporal granularity and maintaining high accuracy in flow performance metrics. This approach shows promise in improving both simulation efficiency and accuracy, offering a valuable tool for network operators.
