@inproceedings{352e0644dec043b399cd194616468eee,
title = "Quanta: Scaling Packet-Level Network Simulation by Exploiting Execution Redundancy",
abstract = "Packet-level network simulation provides high-fidelity modeling but suffers from severe scalability bottlenecks. Existing scaling approaches remain inefficient for modern data-center and AI-training networks. Spatial parallelism requires substantial hardware resources, while temporal-skipping approaches become less effective under bursty traffic. We observe that homogeneous data-center deployments introduce substantial execution redundancy during simulation. This paper presents Quanta, a redundancy-aware simulation framework that eliminates repeated execution in packet-level simulation. Quanta reduces the dependence of simulation cost on physical network scale. Our evaluation shows that Quanta accelerates large-scale simulations by up to 90 {\texttimes}, synergizes with parallel execution for a 320 {\texttimes} combined speedup.",
author = "Jiajun Luan and Hao Li and Yihan Dang and Ze Xia and Danfeng Shan and Peng Zhang",
note = "Publisher Copyright: {\textcopyright} 2026 Copyright held by the owner/author(s).; 10th Asia-Pacific Workshop on Networking, APNet 2026 ; Conference date: 06-08-2026 Through 07-08-2026",
year = "2026",
month = aug,
day = "5",
doi = "10.1145/3820441.3821239",
language = "英语",
series = "APNet 2026 - Proceedings of the 10th Asia-Pacific Workshop on Networking",
publisher = "Association for Computing Machinery, Inc",
pages = "315--317",
booktitle = "APNet 2026 - Proceedings of the 10th Asia-Pacific Workshop on Networking",
}