Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31731
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dc.contributor.authorLi, T-
dc.contributor.authorLiu, W-
dc.contributor.authorMa, X-
dc.contributor.authorZhu, S-
dc.contributor.authorCao, J-
dc.contributor.authorXu, D-
dc.contributor.authorYang, Z-
dc.contributor.authorLiu, S-
dc.contributor.authorZhang, T-
dc.contributor.authorZhu, Y-
dc.contributor.authorWu, B-
dc.contributor.authorWang, K-
dc.contributor.authorXu, K-
dc.date.accessioned2025-08-12T15:08:35Z-
dc.date.available2025-08-12T15:08:35Z-
dc.date.issued2025-04-08-
dc.identifierORCiD: Tong Li https://orcid.org/0000-0002-6805-9565-
dc.identifierORCiD: Wei Liu https://orcid.org/0009-0008-1076-9004-
dc.identifierORCiD: Xinyu Ma https://orcid.org/0009-0002-2930-4334-
dc.identifierORCiD: Shuaipeng Zhu https://orcid.org/0009-0002-4883-7769-
dc.identifierORCiD: Jingkun Cao https://orcid.org/0009-0000-1075-8091-
dc.identifierORCiD: Duling Xu https://orcid.org/0009-0007-3090-5838-
dc.identifierORCiD: Zhaoqi Yang https://orcid.org/0009-0004-3469-3789-
dc.identifierORCiD: Senzhen Liu https://orcid.org/0009-0007-8010-8941-
dc.identifierORCiD: Taotao Zhang https://orcid.org/0009-0008-5102-1684-
dc.identifierORCiD: Yinfeng Zhu https://orcid.org/0009-0003-6022-9464-
dc.identifierORCiD: Bo Wu https://orcid.org/0000-0002-3914-2415-
dc.identifierORCiD: Kezhi Wang https://orcid.org/0000-0001-8602-0800-
dc.identifierORCiD: Ke Xu https://orcid.org/0000-0003-2587-8517-
dc.identifier.citationLi, T. et al. (2025) 'Accelerating Loss Recovery for Content Delivery Network', IEEE Transactions on Computers, 74 (7), pp. 2223 - 2237. doi: 10.1109/TC.2025.3558020.en_US
dc.identifier.issn0018-9340-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/31731-
dc.descriptionThis article has supplementary downloadable material available at https://doi.org/10.1109/TC.2025.3558020, provided by the authors. Digital Object Identifier 10.1109/TC.2025.3558020en_US
dc.description.abstractPacket losses significantly impact the user experience of content delivery network (CDN) services such as live streaming and data backup-and-archiving. However, our production network measurement studies show that the legacy loss recovery is far from satisfactory due to the wide-area loss characteristics (i.e., dynamics and burstiness) in the wild. In this paper, we propose a sender-side Adaptive ReTransmission scheme, ART, which minimizes the recovery time of lost packets with minimal redundancy cost. Distinguishing itself from forward-error-correction (FEC), which preemptively sends redundant data packets to prevent loss, ART functions as an automatic-repeat-request (ARQ) scheme. It applies redundancy specifically to lost packets instead of unlost packets, thereby addressing the characteristic patterns of wide-area losses in real-world scenarios. We implement ART upon QUIC protocol and evaluate it via both trace-driven emulation and real-world deployment. The results show that ART reduces up to 34% of flow completion time (FCT) for delay-sensitive transmissions, improves up to 26% of goodput for throughput-intensive transmissions, reduces 11.6% video playback rebuffering, and saves up to 90% of redundancy cost.en_US
dc.description.sponsorship10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62202473 and 62441230); Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant Number: 62221003); Key Program of the National Natural Science Foundation of China (Grant Number: 61932016 and 62132011); National Science Foundation for Distinguished Young Scholars of China (Grant Number: 62425201).en_US
dc.format.extent2223 - 2237-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.urihttps://doi.org/10.1109/TC.2025.3558020-
dc.rightsCopyright © 2025 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works ( https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ ).-
dc.rights.urihttps://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/-
dc.subjectloss recoveryen_US
dc.subjectQUICen_US
dc.subjectwide-area networken_US
dc.subjectreinforcement learningen_US
dc.subjectARQen_US
dc.subjectFECen_US
dc.titleAccelerating Loss Recovery for Content Delivery Networken_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/TC.2025.3558020-
dc.relation.isPartOfIEEE Transactions on Computers-
pubs.issue7-
pubs.publication-statusPublished-
pubs.volume74-
dc.identifier.eissn1557-9956-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
Appears in Collections:Dept of Computer Science Research Papers

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