research-article

Server selection in large-scale video-on-demand systems

Authors:

Niklas Carlsson,

Derek L. EagerAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 6, Issue 1

Article No.: 1, Pages 1 - 26

https://doi.org/10.1145/1671954.1671955

Published: 22 February 2010 Publication History

Abstract

Video on demand, particularly with user-generated content, is emerging as one of the most bandwidth-intensive applications on the Internet. Owing to content control and other issues, some video-on-demand systems attempt to prevent downloading and peer-to-peer content delivery. Instead, such systems rely on server replication, such as via third-party content distribution networks, to support video streaming (or pseudostreaming) to their clients. A major issue with such systems is the cost of the required server resources.

By synchronizing the video streams for clients that make closely spaced requests for the same video from the same server, server costs (such as for retrieval of the video data from disk) can be amortized over multiple requests. A fundamental trade-off then arises, however, with respect to server selection. Network delivery cost is minimized by selecting the nearest server, while server cost is minimized by directing closely spaced requests for the same video to a common server.

This article compares classes of server selection policies within the context of a simple system model. We conclude that: (i) server selection using dynamic system state information (rather than only proximities and average loads) can yield large improvements in performance, (ii) deferring server selection for a request as late as possible (i.e., until just before streaming is to begin) can yield additional large improvements, and (iii) within the class of policies using dynamic state information and deferred selection, policies using only “local” (rather than global) request information are able to achieve most of the potential performance gains.

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Shi WLi QZhang RShen GJiang YYuan ZMuntean GShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)QoE Ready to Respond: A QoE-aware MEC Selection Scheme for DASH-based Adaptive Video Streaming to Mobile UsersProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475325(4016-4024)Online publication date: 17-Oct-2021
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Index Terms

Server selection in large-scale video-on-demand systems

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 6, Issue 1

February 2010

138 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/1671954

Issue’s Table of Contents

Copyright © 2010 ACM.

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Association for Computing Machinery

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Publication History

Published: 22 February 2010

Accepted: 01 September 2008

Revised: 01 July 2008

Received: 01 April 2008

Published in TOMM Volume 6, Issue 1

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Cited By

Shi WLi QZhang RShen GJiang YYuan ZMuntean GShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)QoE Ready to Respond: A QoE-aware MEC Selection Scheme for DASH-based Adaptive Video Streaming to Mobile UsersProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475325(4016-4024)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3475325
Deshpande PSharma SPeddoju SDeshpande PSharma SPeddoju S(2019)Data Security and Storage Module in CloudSecurity and Data Storage Aspect in Cloud Computing10.1007/978-981-13-6089-3_4(49-70)Online publication date: 10-Feb-2019
https://doi.org/10.1007/978-981-13-6089-3_4
Govindan KArunachalam KSubramaniam K(2018)Optimal server selection policy for improved network efficiency in smart phones2018 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC.2018.8376947(1-6)Online publication date: 15-Apr-2018
https://dl.acm.org/doi/10.1109/WCNC.2018.8376947
Machanick PMasinde M(2017)Video on demand in a high bandwidth worldProceedings of the South African Institute of Computer Scientists and Information Technologists10.1145/3129416.3129424(1-8)Online publication date: 26-Sep-2017
https://dl.acm.org/doi/10.1145/3129416.3129424
Zhang WMo ZChen CZheng Q(2014)CBCMultimedia Tools and Applications10.5555/2687511.268755973:3(1663-1686)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.5555/2687511.2687559
Wan JLiu LGuo J(2014)Dynamic Request Routing for Online Video-on-Demand Service: A Markov Decision Process ApproachMathematical Problems in Engineering10.1155/2014/9208292014(1-13)Online publication date: 2014
https://doi.org/10.1155/2014/920829
Ni WLiu RBiswas JWang XCollings IJha S(2014)Multiuser MIMO Scheduling for Mobile Video ApplicationsIEEE Transactions on Wireless Communications10.1109/TWC.2014.234797313:10(5382-5395)Online publication date: Oct-2014
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Badea RBorcoci E(2014)Content server saturation avoidance algorithm based on financial analysis techniques2014 22nd Telecommunications Forum Telfor (TELFOR)10.1109/TELFOR.2014.7034373(127-130)Online publication date: Nov-2014
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Carlsson NEager DGopinathan ALi Z(2014)Caching and optimized request routing in cloud-based content delivery systemsPerformance Evaluation10.1016/j.peva.2014.07.00379(38-55)Online publication date: Sep-2014
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Ren DChan SShi GZhang H(2014)Distributed joint optimization for large-scale video-on-demandComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2014.09.01475:PA(86-98)Online publication date: 24-Dec-2014
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