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Practical, Real-time Centralized Control for CDN-based Live Video Delivery

Authors:

Matthew K. Mukerjee,

Srinivasan Seshan,

Hui ZhangAuthors Info & Claims

SIGCOMM '15: Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication

Pages 311 - 324

https://doi.org/10.1145/2785956.2787475

Published: 17 August 2015 Publication History

Abstract

Live video delivery is expected to reach a peak of 50 Tbps this year. This surging popularity is fundamentally changing the Internet video delivery landscape. CDNs must meet users' demands for fast join times, high bitrates, and low buffering ratios, while minimizing their own cost of delivery and responding to issues in real-time. Wide-area latency, loss, and failures, as well as varied workloads ("mega-events" to long-tail), make meeting these demands challenging.

An analysis of video sessions concluded that a centralized controller could improve user experience, but CDN systems have shied away from such designs due to the difficulty of quickly handling failures, a requirement of both operators and users. We introduce VDN, a practical approach to a video delivery network that uses a centralized algorithm for live video optimization. VDN provides CDN operators with real-time, fine-grained control. It does this in spite of challenges resulting from the wide-area (e.g., state inconsistency, partitions, failures) by using a hybrid centralized+distributed control plane, increasing average bitrate by 1.7x and decreasing cost by 2x in different scenarios.

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Index Terms

Practical, Real-time Centralized Control for CDN-based Live Video Delivery
1. Networks
  1. Network algorithms
    1. Control path algorithms
      1. Traffic engineering algorithms
  2. Network types
    1. Overlay and other logical network structures

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cover image ACM Conferences

SIGCOMM '15: Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication

August 2015

684 pages

ISBN:9781450335423

DOI:10.1145/2785956

General Chairs:
Steve Uhlig
Queen Mary University of London, UK
,
Olaf Maennel
Tallinn U. of Technology in Estonia, Estonia
,
Program Chairs:
Brad Karp
University College London, UK
,
Jitendra Padhye
Microsoft, USA

ACM SIGCOMM Computer Communication Review Volume 45, Issue 4
SIGCOMM'15
October 2015
659 pages
ISSN:0146-4833
DOI:10.1145/2829988
Editors:
Konstantina Papagiannaki
Telefonica Research, Barcelona, Spain
,
Katerina Argyraki
EPFL, Switzerland
,
Hitesh Ballani
Microsoft Research Cambridge, UK
,
Fabián Bustamante
Northwestern University, USA
,
Joseph Camp
SMU, USA
,
Augustin Chaintreau
Columbia University, USA
,
Phillipa Gill
Stony Brook University, USA
,
Marco Mellia
Politecnico di Torino, Italy
,
Bhaskaran Raman
IIT Bombay, India
,
Joel Sommers
Colgate University, USA
,
Aline Carneiro Viana
INRIA, France
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Published: 17 August 2015

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SIGCOMM '15: ACM SIGCOMM 2015 Conference

August 17 - 21, 2015

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SIGCOMM '15 Paper Acceptance Rate 40 of 242 submissions, 17%;

Overall Acceptance Rate 462 of 3,389 submissions, 14%

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https://doi.org/10.1109/CSCWD61410.2024.10580564
Song JChen BSong BYing AHu Y(2023)FlowBot: A Learning-Based Co-bottleneck Flow Detector for Video Servers2023 IEEE 31st International Conference on Network Protocols (ICNP)10.1109/ICNP59255.2023.10355638(1-12)Online publication date: 10-Oct-2023
https://doi.org/10.1109/ICNP59255.2023.10355638
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