Scalable BGP Simulation of Hyper-Scale Data Center Networks

Modern cloud data center networks (DCNs) are hyper-scale, consisting of tens of thousands of switches, which poses significant challenges for simulation-based control-plane verification. Existing simulation tools face three key limitations when simulating BGP in such large-scale DCNs: poor scalability in large topologies, inaccuracy under non-monotonic configurations, and a lack of support for incremental simulation. To overcome these challenges, we present vBGPSim, a versatile BGP simulator designed for hyper-scale DCNs. vBGPSim enhances scalability through topology compression and a generalized Dijkstra's algorithm that replaces the iterative algorithm. It improves simulation accuracy under the non-monotonic configurations by integrating a withdrawal mechanism into Dijkstra's algorithm and supports incremental simulation using the same mechanism. Extensive evaluations on production and synthetic DCNs show that vBGPSim scales to networks with over 10,000 switches, while accurately handling non-monotonic updates and supporting efficient incremental simulation.

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Publication Details

Published
2025-08-19
DOI
https://doi.org/10.1145/3750022.3750455
Primary Topic
Cloud Computing and Resource Management
Type
article
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article

Scalable BGP Simulation of Hyper-Scale Data Center Networks

Haihua Li, Lizhao You, X. Y. Zheng, Qiao Xiang et al.
Cloud Computing and Resource Management
article

Scalable BGP Simulation of Hyper-Scale Data Center Networks

Haihua Li, Lizhao You, X. Y. Zheng, Qiao Xiang, Mengrui Zhang, Ruojian Wen, Zhi Zhang, Yang Wang, Yuanxun Kang, Fei Yuan, Yongwang Zhong, Z. P. Yao, Ronghua Sun
article en

Abstract

Modern cloud data center networks (DCNs) are hyper-scale, consisting of tens of thousands of switches, which poses significant challenges for simulation-based control-plane verification. Existing simulation tools face three key limitations when simulating BGP in such large-scale DCNs: poor scalability in large topologies, inaccuracy under non-monotonic configurations, and a lack of support for incremental simulation. To overcome these challenges, we present vBGPSim, a versatile BGP simulator designed for hyper-scale DCNs. vBGPSim enhances scalability through topology compression and a generalized Dijkstra's algorithm that replaces the iterative algorithm. It improves simulation accuracy under the non-monotonic configurations by integrating a withdrawal mechanism into Dijkstra's algorithm and supports incremental simulation using the same mechanism. Extensive evaluations on production and synthetic DCNs show that vBGPSim scales to networks with over 10,000 switches, while accurately handling non-monotonic updates and supporting efficient incremental simulation.

Xiamen University (CN), Huawei Technologies (China) (CN), Abeam Technologies (United States) (US)
Openalex Percentile: Top 68%
Cloud Computing and Resource Management
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Scalable BGP Simulation of Hyper-Scale Data Center Networks — Haihua Li, Lizhao You, et al. · (2025) | TGRS Research Map | TGRS