m4: A Learned Flow-level Network Simulator
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915180533776384 |
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| author | Li, Chenning Zabreyko, Anton A. Nasr-Esfahany, Arash Zhao, Kevin Goyal, Prateesh Alizadeh, Mohammad Anderson, Thomas |
| author_facet | Li, Chenning Zabreyko, Anton A. Nasr-Esfahany, Arash Zhao, Kevin Goyal, Prateesh Alizadeh, Mohammad Anderson, Thomas |
| contents | Flow-level simulation is widely used to model large-scale data center networks due to its scalability. Unlike packet-level simulators that model individual packets, flow-level simulators abstract traffic as continuous flows with dynamically assigned transmission rates. While this abstraction enables orders-of-magnitude speedup, it is inaccurate by omitting critical packet-level effects such as queuing, congestion control, and retransmissions.
We present m4, an accurate and scalable flow-level simulator that uses machine learning to learn the dynamics of the network of interest. At the core of m4 lies a novel ML architecture that decomposes state transition computations into distinct spatial and temporal components, each represented by a suitable neural network. To efficiently learn the underlying flow-level dynamics, m4 adds dense supervision signals by predicting intermediate network metrics such as remaining flow size and queue length during training. m4 achieves a speedup of up to 104$\times$ over packet-level simulation. Relative to a traditional flow-level simulation, m4 reduces per-flow estimation errors by 45.3% (mean) and 53.0% (p90). For closed-loop applications, m4 accurately predicts network throughput under various congestion control schemes and workloads. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01770 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | m4: A Learned Flow-level Network Simulator Li, Chenning Zabreyko, Anton A. Nasr-Esfahany, Arash Zhao, Kevin Goyal, Prateesh Alizadeh, Mohammad Anderson, Thomas Networking and Internet Architecture Machine Learning Performance Systems and Control Flow-level simulation is widely used to model large-scale data center networks due to its scalability. Unlike packet-level simulators that model individual packets, flow-level simulators abstract traffic as continuous flows with dynamically assigned transmission rates. While this abstraction enables orders-of-magnitude speedup, it is inaccurate by omitting critical packet-level effects such as queuing, congestion control, and retransmissions. We present m4, an accurate and scalable flow-level simulator that uses machine learning to learn the dynamics of the network of interest. At the core of m4 lies a novel ML architecture that decomposes state transition computations into distinct spatial and temporal components, each represented by a suitable neural network. To efficiently learn the underlying flow-level dynamics, m4 adds dense supervision signals by predicting intermediate network metrics such as remaining flow size and queue length during training. m4 achieves a speedup of up to 104$\times$ over packet-level simulation. Relative to a traditional flow-level simulation, m4 reduces per-flow estimation errors by 45.3% (mean) and 53.0% (p90). For closed-loop applications, m4 accurately predicts network throughput under various congestion control schemes and workloads. |
| title | m4: A Learned Flow-level Network Simulator |
| topic | Networking and Internet Architecture Machine Learning Performance Systems and Control |
| url | https://arxiv.org/abs/2503.01770 |