PreTE: Traffic Engineering with Predictive Failures
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3718958.3750508.pdf
Size
3.85 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Miao, Congcong
Zhong, Zhizhen
Zhao, Yiren
Gupta, Arpit
Zhang, Ying
Li, Sirui
He, Zekun
Zou, Xianneng
Wang, Jilong
Date Issued
August 27, 2025
Publisher
ACM|ACM SIGCOMM 2025 Conference
Citation
Congcong Miao, Zhizhen Zhong, Yiren Zhao, Arpit Gupta, Ying Zhang, Sirui Li, Zekun He, Xianneng Zou, and Jilong Wang. 2025. PreTE: Traffic Engineering with Predictive Failures. In Proceedings of the ACM SIGCOMM 2025 Conference (SIGCOMM '25). Association for Computing Machinery, New York, NY, USA, 780–795.
Version
Final published version
Abstract
Fiber links in wide-area networks (WANs) are exposed to complicated environments and hence are vulnerable to failures like fiber cuts. The conventional approach of using static probabilistic failures falls short in fiber-cut scenarios because these fiber cuts are rare but disruptive, making it difficult for network operators to balance network utilization and availability in WAN traffic engineering. Our large-scale measurements of per-second optical-layer data reveal that the fiber's failure probability increases by several orders of magnitude when experiencing a rare and ephemeral degradation state. Therefore, we present a novel traffic engineering (TE) system called PreTE to factor in the dynamic fiber cut probabilities directly into TE systems. At the core of the PreTE system, fiber degradation facilitates failure predictions and traffic tunnels to be proactively updated, followed by traffic allocation optimizations among updated tunnels. We evaluate PreTE using a production-level WAN testbed and large-scale simulations. The testbed evaluation quantifies PreTE's runtime to demonstrate the feasibility to implement in large-scale WANs. Our large-scale simulation results show that PreTE can support up to 2× more demand at the same level of availability as compared to existing TE schemes.
Description
SIGCOMM ’25, September 8–11, 2025, Coimbra, Portugal
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3718958.3750508
