RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning
This work addresses network modeling challenges for network operators by offering a more efficient and accurate simulation tool, though it appears incremental as it builds on existing ML-based approaches.
The paper tackles the computational cost and accuracy limitations of traditional network simulation methods like Discrete Event Simulation (DES) by introducing RouteNet-Gauss, which integrates a testbed network with a machine learning model to achieve up to 95% error reduction and a 488x speedup in inference time.
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.