GenNP: A low-threshold and powerful network performance data generator

Abstract

The existing Discrete Event Simulators (DES) cannot meet the demands of modern networks for efficient, accurate, and flexible simulation. Recent machine learning models have demonstrated exceptional capabilities for estimating network performance (MLENP). However, the quality and quantity of available data greatly limit the accuracy and generalizability of ML models. After analyzing the data requirements of MLENP over the past decade and the shortcomings of existing DES, we propose a low-threshold and powerful network performance data generator (GenNP), and generate a network performance dataset consisting of 10K samples. GenNP, with OMNeT＋＋ and INET at its simulation core, integrates the configuration generation layer, simulation transformation layer, result extraction layer, and result output layer, achieving massive random generation of simulation configurations (networks, traffic, routing protocols, faults) and multi-granularity extraction of network performance data (throughput, drop, delay, jitter, routing table). We validate the robust capabilities of GenNP through a series of simulation experiments across multi-granularity (spatial, temporal), diversity (traffic models, network load, fault types, routing protocols), and efficiency (parallelism).