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%.
Ensuring the reliability of quantum networks requires the accurate evaluation of fidelity-a metric representing link quality-and the selection of high-fidelity links. However, since fidelity estimation consumes a large number of measurements, an efficient method for identifying high-quality communication links with limited resources is desirable. Existing studies have primarily focused on identifying high-fidelity links between a pair of nodes, leaving adaptive resource allocation insufficiently explored in multi-destination environments where destinations differ in network importance (e.g., communication demand or the quantum memory capacity of nodes). In this study, we propose DaTopLinks (Demand-aware Top- $K$ HighFidelity Links), a method for efficiently identifying the top- $K$ destinations and their best link for each selected destination according to a utility function that combines destination importance and link fidelity. The algorithm introduces a dual-criterion link elimination mechanism that simultaneously performs intradestination link elimination and top- $K$ destination elimination. It also incorporates an early confirmation mechanism that allows early termination of measurements for destinations once their inclusion in the top- $K$ set and the identification of their best link are statistically guaranteed. In our theoretical analysis, we derive an upper bound on the sample complexity based on an effective gap that captures both inter-destination utility differences and intra-destination fidelity differences. Simulation results demonstrate that the proposed method functions effectively under depolarizing, dephasing, and bit-flip noise models.
Shun Yamachika, Yuto Kakihara, Shota Inoue et al.· Annual International Compute...· 0 citations