Adaptive Step Size Control for Accelerating Token-Based Flow-Level Network Simulation

Abstract

In the performance evaluation of communication networks, packet-level simulation entails enormous computational costs under conditions such as large-scale networks or numerous communicating flows, leading to issues where execution time exceeds practical limits. In contrast, flow-level simulation, which approximates traffic as a continuous fluid, can mitigate the increase in computational complexity associated with the growing number of packets. We have previously proposed a method to accelerate simulations by circulating a token, which carries information regarding the network state, along the path traversed by a flow during the simulation. However, existing token-based simulators assume a fixed step size and perform state updates at constant intervals, even during periods where state changes are minimal. Consequently, redundant computations occur relative to the required accuracy. In this study, we propose a method that introduces dynamic step size control to a token-based flow-level simulator. To address the structural issue of token history omissions caused by step size alterations, we propose a hybrid step-doubling method that estimates missing token information through token interpolation and direct calculation of internal states, thereby autonomously controlling the step size. Evaluation experiments conducted in a simple environment using a dumbbell network topology with a single bottleneck confirmed the effectiveness of the proposed method. Compared to the conventional fixed step size method, the simulation execution time was reduced by approximately 90% to 97%, and memory consumption was reduced by approximately 25% to 70%.