NGSim: A High-Fidelity and Efficient Simulator for Optimizing Network Function Graphs

摘要

High-performance network functions (NFs) are typically designed with modularization, consolidation, and vectorization, forming NF graphs. However, optimizing vectorized NF graphs is challenging due to complex interactions among workload diversity, packet characteristics, and hardware constraints. While intelligent algorithms are promising, tuning them directly on real systems is often impractical due to significant engineering overhead and hardware costs. We argue that a simulator that is interactive, accurate, and efficient is essential for enabling effective NF-graph optimization.We present NGSim, a simulator designed to meet these goals through a hybrid event- and data-driven approach with a decoupled, parallel simulation pipeline. NGSim enables fast and accurate exploration of NF-graph performance under diverse conditions. Evaluation shows that it achieves an average relative error of only 1.38% for throughput and an absolute error of 0.03% for SLO violation rate, while reducing simulation time by 85% compared to its sequential baseline. A case study with reinforcement learning further demonstrates NGSim’s effectiveness, yielding an 11.5% improvement in throughput without violating SLOs.