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LiveJack: Integrating CDNs and Edge Clouds for Live Content Broadcasting

Authors:

H. Jonathan ChaoAuthors Info & Claims

MM '17: Proceedings of the 25th ACM international conference on Multimedia

Pages 73 - 81

https://doi.org/10.1145/3123266.3123283

Published: 19 October 2017 Publication History

Abstract

Emerging commercial live content broadcasting platforms are facing great challenges to accommodate large scale dynamic viewer populations. Existing solutions constantly suffer from balancing the cost of deploying at the edge close to the viewers and the quality of content delivery. We propose LiveJack, a novel network service to allow CDN servers to seamlessly leverage ISP edge cloud resources. LiveJack can elastically scale the serving capacity of CDN servers by integrating Virtual Media Functions (VMF) in the edge cloud to accommodate flash crowds for very popular contents. LiveJack introduces minor application layer changes for streaming service providers and is completely transparent to end users. We have prototyped LiveJack in both LAN and WAN environments. Evaluations demonstrate that LiveJack can increase CDN server capacity by more than six times, and can effectively accommodate highly dynamic workloads with an improved service quality.

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Cited By

Zhang RYang CWang XHuang TWu CLiu JSun L(2023)Practical Cloud-Edge Scheduling for Large-Scale Crowdsourced Live StreamingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.326773134:7(2055-2071)Online publication date: Jul-2023
https://doi.org/10.1109/TPDS.2023.3267731
Liu XDerakhshani MMihaylova LLambotharan S(2023)Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live StreamingIEEE Journal on Selected Areas in Communications10.1109/JSAC.2022.322942341:3(740-754)Online publication date: Mar-2023
https://doi.org/10.1109/JSAC.2022.3229423
Liborio JMelo CSilva M(2022)Internet Video Delivery Improved by Super-Resolution with GANFuture Internet10.3390/fi1412036414:12(364)Online publication date: 6-Dec-2022
https://doi.org/10.3390/fi14120364
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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '17: Proceedings of the 25th ACM international conference on Multimedia

October 2017

2028 pages

ISBN:9781450349062

DOI:10.1145/3123266

General Chairs:
Qiong Liu
FXPAL, USA
,
Rainer Lienhart
Universität Augsburg, Germany
,
Haohong Wang
TCL America, USA
,
Program Chairs:
Sheng-Wei "Kuan-Ta" Chen
Academia Sinica, Taiwan
,
Susanne Boll
University of Oldenburg, Germany
,
Phoebe Chen
La Trobe University, Australia
,
Gerald Friedland
Lawrence Livermore National Lab, USA
,
Jia Li
Google, USA
,
Shuicheng Yan
Qihoo 360, China

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2017

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MM '17: ACM Multimedia Conference

October 23 - 27, 2017

California, Mountain View, USA

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MM '17 Paper Acceptance Rate 189 of 684 submissions, 28%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Zhang RYang CWang XHuang TWu CLiu JSun L(2023)Practical Cloud-Edge Scheduling for Large-Scale Crowdsourced Live StreamingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.326773134:7(2055-2071)Online publication date: Jul-2023
https://doi.org/10.1109/TPDS.2023.3267731
Liu XDerakhshani MMihaylova LLambotharan S(2023)Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live StreamingIEEE Journal on Selected Areas in Communications10.1109/JSAC.2022.322942341:3(740-754)Online publication date: Mar-2023
https://doi.org/10.1109/JSAC.2022.3229423
Liborio JMelo CSilva M(2022)Internet Video Delivery Improved by Super-Resolution with GANFuture Internet10.3390/fi1412036414:12(364)Online publication date: 6-Dec-2022
https://doi.org/10.3390/fi14120364
Wang YHsin WLamsal MMerkle LDoyle MSheard JSoh LDorn B(2022)EdGENIProceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499300(801-807)Online publication date: 22-Feb-2022
https://dl.acm.org/doi/10.1145/3478431.3499300
Wang HTang GWu KWang J(2022)PLVER: Joint Stable Allocation and Content Replication for Edge-Assisted Live Video DeliveryIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.309078433:1(218-230)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TPDS.2021.3090784
Chen XXu CWang MWu ZZhong LGrieco L(2021)Augmented Queue-Based Transmission and Transcoding Optimization for Livecast Services Based on Cloud-Edge-Crowd IntegrationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.304785931:11(4470-4484)Online publication date: Nov-2021
https://doi.org/10.1109/TCSVT.2020.3047859
Li ZDrew MLiu JLi ZDrew MLiu J(2021)Online Social Media SharingFundamentals of Multimedia10.1007/978-3-030-62124-7_19(707-735)Online publication date: 17-Feb-2021
https://doi.org/10.1007/978-3-030-62124-7_19
Zhang RMa MHuang TPang HYao XWu CLiu JSun LAmsaleg LHuet BLarson MGravier GHung HNgo CTsang Ooi W(2019)LivesmartProceedings of the 27th ACM International Conference on Multimedia10.1145/3343031.3351013(420-428)Online publication date: 15-Oct-2019
https://dl.acm.org/doi/10.1145/3343031.3351013
Khalid AZahran ASreenan CZink MToni LBegen A(2019)An SDN-based device-aware live video service for inter-domain adaptive bitrate streamingProceedings of the 10th ACM Multimedia Systems Conference10.1145/3304109.3306229(121-132)Online publication date: 18-Jun-2019
https://dl.acm.org/doi/10.1145/3304109.3306229
Wang FZhang Cwang FLiu JZhu YPang HSun L(2019)Intelligent Edge-Assisted Crowdcast with Deep Reinforcement Learning for Personalized QoEIEEE INFOCOM 2019 - IEEE Conference on Computer Communications10.1109/INFOCOM.2019.8737456(910-918)Online publication date: Apr-2019
https://doi.org/10.1109/INFOCOM.2019.8737456
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