Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31731
Title: Accelerating Loss Recovery for Content Delivery Network
Authors: Li, T
Liu, W
Ma, X
Zhu, S
Cao, J
Xu, D
Yang, Z
Liu, S
Zhang, T
Zhu, Y
Wu, B
Wang, K
Xu, K
Keywords: loss recovery;QUIC;wide-area network;reinforcement learning;ARQ;FEC
Issue Date: 8-Apr-2025
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Li, 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.
Abstract: Packet 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.
Description: This 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.2025.3558020
URI: https://bura.brunel.ac.uk/handle/2438/31731
DOI: https://doi.org/10.1109/TC.2025.3558020
ISSN: 0018-9340
Other Identifiers: ORCiD: Tong Li https://orcid.org/0000-0002-6805-9565
ORCiD: Wei Liu https://orcid.org/0009-0008-1076-9004
ORCiD: Xinyu Ma https://orcid.org/0009-0002-2930-4334
ORCiD: Shuaipeng Zhu https://orcid.org/0009-0002-4883-7769
ORCiD: Jingkun Cao https://orcid.org/0009-0000-1075-8091
ORCiD: Duling Xu https://orcid.org/0009-0007-3090-5838
ORCiD: Zhaoqi Yang https://orcid.org/0009-0004-3469-3789
ORCiD: Senzhen Liu https://orcid.org/0009-0007-8010-8941
ORCiD: Taotao Zhang https://orcid.org/0009-0008-5102-1684
ORCiD: Yinfeng Zhu https://orcid.org/0009-0003-6022-9464
ORCiD: Bo Wu https://orcid.org/0000-0002-3914-2415
ORCiD: Kezhi Wang https://orcid.org/0000-0001-8602-0800
ORCiD: Ke Xu https://orcid.org/0000-0003-2587-8517
Appears in Collections:Dept of Computer Science Research Papers

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