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 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © 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/ ). | 1.39 MB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.