dc.contributor.author | Miao, Congcong | |
dc.contributor.author | Zhong, Zhizhen | |
dc.contributor.author | Zhao, Yiren | |
dc.contributor.author | Gupta, Arpit | |
dc.contributor.author | Zhang, Ying | |
dc.contributor.author | Li, Sirui | |
dc.contributor.author | He, Zekun | |
dc.contributor.author | Zou, Xianneng | |
dc.contributor.author | Wang, Jilong | |
dc.date.accessioned | 2025-09-10T19:48:10Z | |
dc.date.available | 2025-09-10T19:48:10Z | |
dc.date.issued | 2025-08-27 | |
dc.identifier.isbn | 979-8-4007-1524-2 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/162641 | |
dc.description | SIGCOMM ’25, September 8–11, 2025, Coimbra, Portugal | en_US |
dc.description.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. | en_US |
dc.publisher | ACM|ACM SIGCOMM 2025 Conference | en_US |
dc.relation.isversionof | https://doi.org/10.1145/3718958.3750508 | en_US |
dc.rights | 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. | en_US |
dc.source | Association for Computing Machinery | en_US |
dc.title | PreTE: Traffic Engineering with Predictive Failures | en_US |
dc.type | Article | en_US |
dc.identifier.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. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US |
dc.identifier.mitlicense | PUBLISHER_POLICY | |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dc.date.updated | 2025-09-01T07:54:31Z | |
dc.language.rfc3066 | en | |
dc.rights.holder | The author(s) | |
dspace.date.submission | 2025-09-01T07:54:32Z | |
mit.license | PUBLISHER_POLICY | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |