Show simple item record

dc.contributor.authorMiao, Congcong
dc.contributor.authorZhong, Zhizhen
dc.contributor.authorZhao, Yiren
dc.contributor.authorGupta, Arpit
dc.contributor.authorZhang, Ying
dc.contributor.authorLi, Sirui
dc.contributor.authorHe, Zekun
dc.contributor.authorZou, Xianneng
dc.contributor.authorWang, Jilong
dc.date.accessioned2025-09-10T19:48:10Z
dc.date.available2025-09-10T19:48:10Z
dc.date.issued2025-08-27
dc.identifier.isbn979-8-4007-1524-2
dc.identifier.urihttps://hdl.handle.net/1721.1/162641
dc.descriptionSIGCOMM ’25, September 8–11, 2025, Coimbra, Portugalen_US
dc.description.abstractFiber 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.publisherACM|ACM SIGCOMM 2025 Conferenceen_US
dc.relation.isversionofhttps://doi.org/10.1145/3718958.3750508en_US
dc.rightsArticle 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.sourceAssociation for Computing Machineryen_US
dc.titlePreTE: Traffic Engineering with Predictive Failuresen_US
dc.typeArticleen_US
dc.identifier.citationCongcong 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.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.identifier.mitlicensePUBLISHER_POLICY
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2025-09-01T07:54:31Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2025-09-01T07:54:32Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record