Performance Analysis of Resource Selection Schemes for a Large Scale Video-on-Demand System

The designers of a large scale video-on-demand system face an optimization problem of deciding how to assign movies to multiple disks (servers) such that the request blocking probability is minimized subject to capacity constraints. To solve this problem, it is essential to develop scalable and accu...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on multimedia 2008-01, Vol.10 (1), p.153-159
Main Authors: Jun Guo, Wong, E.W.M., Chan, S., Taylor, P., Zukerman, M., Kit-Sang Tang
Format: Article
Language:English
Subjects:
Applied sciences
Blocking probability
Capacity planning
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Constraint optimization
Design optimization
Disks
Exact sciences and technology
fixed-point approximation
Large-scale systems
Mathematical analysis
Mathematics
Motion pictures
Multimedia
Optimization
Performance analysis
Performance evaluation
resource selection
Servers
Software
Statistics
Tasks
Testing
Video on demand
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c391t-3966a0a6bc0edef3bde7ff03a42fbb8bcf6a472944917d48e635c3c191fea8b53
cites cdi_FETCH-LOGICAL-c391t-3966a0a6bc0edef3bde7ff03a42fbb8bcf6a472944917d48e635c3c191fea8b53
container_end_page 159
container_issue 1
container_start_page 153
container_title IEEE transactions on multimedia
container_volume 10
creator Jun Guo
Wong, E.W.M.
Chan, S.
Taylor, P.
Zukerman, M.
Kit-Sang Tang
description The designers of a large scale video-on-demand system face an optimization problem of deciding how to assign movies to multiple disks (servers) such that the request blocking probability is minimized subject to capacity constraints. To solve this problem, it is essential to develop scalable and accurate analytical means to evaluate the blocking performance of the system for a given file assignment. The performance analysis is made more complicated by the fact that the request blocking probability depends also on how disks are selected to serve user requests for multicopy movies. In this paper, we analyze several efficient resource selection schemes. Numerical results demonstrate that our analysis is scalable and sufficiently accurate to support the task of file assignment optimization in such a system.
doi_str_mv 10.1109/TMM.2007.911281
format article
fullrecord <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_ieee_primary_4407514</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4407514</ieee_id><sourcerecordid>2327137511</sourcerecordid><originalsourceid>FETCH-LOGICAL-c391t-3966a0a6bc0edef3bde7ff03a42fbb8bcf6a472944917d48e635c3c191fea8b53</originalsourceid><addsrcrecordid>eNpdkE1LxDAQhosouK6ePXgJgnjqOtOkTXMUv2FFcdWLh5KmE620jSa7h_33ZtlFwVPCzDMvM0-SHCJMEEGdPd_fTzIAOVGIWYlbyQiVwDRW5Hb85xmkKkPYTfZC-ARAkYMcJW-P5K3zvR4MsfNBd8vQBuYse6LgFj4WZ9SRmbduYDPzQT0FFnmm2VT799g1uiP22jbkUjeklxSTGjZbhjn1-8mO1V2gg807Tl6ur54vbtPpw83dxfk0NVzhPOWqKDToojZADVleNyStBa5FZuu6rI0ttJCZEkKhbERJBc8NN6jQki7rnI-T03Xul3ffCwrzqm-Doa7TA7lFqEqZQ44CRCSP_5Gf8ch4dYQKDlCUCBE6W0PGuxA82erLt732ywqhWqmuoupqpbpaq44TJ5tYHaIP66PNNvyNKZVLlKtFj9ZcS0S_bSFAxvX4D_kshqE</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>863006810</pqid></control><display><type>article</type><title>Performance Analysis of Resource Selection Schemes for a Large Scale Video-on-Demand System</title><source>IEEE Xplore (Online service)</source><creator>Jun Guo ; Wong, E.W.M. ; Chan, S. ; Taylor, P. ; Zukerman, M. ; Kit-Sang Tang</creator><creatorcontrib>Jun Guo ; Wong, E.W.M. ; Chan, S. ; Taylor, P. ; Zukerman, M. ; Kit-Sang Tang</creatorcontrib><description>The designers of a large scale video-on-demand system face an optimization problem of deciding how to assign movies to multiple disks (servers) such that the request blocking probability is minimized subject to capacity constraints. To solve this problem, it is essential to develop scalable and accurate analytical means to evaluate the blocking performance of the system for a given file assignment. The performance analysis is made more complicated by the fact that the request blocking probability depends also on how disks are selected to serve user requests for multicopy movies. In this paper, we analyze several efficient resource selection schemes. Numerical results demonstrate that our analysis is scalable and sufficiently accurate to support the task of file assignment optimization in such a system.</description><identifier>ISSN: 1520-9210</identifier><identifier>EISSN: 1941-0077</identifier><identifier>DOI: 10.1109/TMM.2007.911281</identifier><identifier>CODEN: ITMUF8</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Blocking probability ; Capacity planning ; Computer science; control theory; systems ; Computer systems and distributed systems. User interface ; Constraint optimization ; Design optimization ; Disks ; Exact sciences and technology ; fixed-point approximation ; Large-scale systems ; Mathematical analysis ; Mathematics ; Motion pictures ; Multimedia ; Optimization ; Performance analysis ; Performance evaluation ; resource selection ; Servers ; Software ; Statistics ; Tasks ; Testing ; Video on demand</subject><ispartof>IEEE transactions on multimedia, 2008-01, Vol.10 (1), p.153-159</ispartof><rights>2008 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c391t-3966a0a6bc0edef3bde7ff03a42fbb8bcf6a472944917d48e635c3c191fea8b53</citedby><cites>FETCH-LOGICAL-c391t-3966a0a6bc0edef3bde7ff03a42fbb8bcf6a472944917d48e635c3c191fea8b53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4407514$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,4024,27923,27924,27925,54796</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=19957175$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Jun Guo</creatorcontrib><creatorcontrib>Wong, E.W.M.</creatorcontrib><creatorcontrib>Chan, S.</creatorcontrib><creatorcontrib>Taylor, P.</creatorcontrib><creatorcontrib>Zukerman, M.</creatorcontrib><creatorcontrib>Kit-Sang Tang</creatorcontrib><title>Performance Analysis of Resource Selection Schemes for a Large Scale Video-on-Demand System</title><title>IEEE transactions on multimedia</title><addtitle>TMM</addtitle><description>The designers of a large scale video-on-demand system face an optimization problem of deciding how to assign movies to multiple disks (servers) such that the request blocking probability is minimized subject to capacity constraints. To solve this problem, it is essential to develop scalable and accurate analytical means to evaluate the blocking performance of the system for a given file assignment. The performance analysis is made more complicated by the fact that the request blocking probability depends also on how disks are selected to serve user requests for multicopy movies. In this paper, we analyze several efficient resource selection schemes. Numerical results demonstrate that our analysis is scalable and sufficiently accurate to support the task of file assignment optimization in such a system.</description><subject>Applied sciences</subject><subject>Blocking probability</subject><subject>Capacity planning</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Constraint optimization</subject><subject>Design optimization</subject><subject>Disks</subject><subject>Exact sciences and technology</subject><subject>fixed-point approximation</subject><subject>Large-scale systems</subject><subject>Mathematical analysis</subject><subject>Mathematics</subject><subject>Motion pictures</subject><subject>Multimedia</subject><subject>Optimization</subject><subject>Performance analysis</subject><subject>Performance evaluation</subject><subject>resource selection</subject><subject>Servers</subject><subject>Software</subject><subject>Statistics</subject><subject>Tasks</subject><subject>Testing</subject><subject>Video on demand</subject><issn>1520-9210</issn><issn>1941-0077</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><recordid>eNpdkE1LxDAQhosouK6ePXgJgnjqOtOkTXMUv2FFcdWLh5KmE620jSa7h_33ZtlFwVPCzDMvM0-SHCJMEEGdPd_fTzIAOVGIWYlbyQiVwDRW5Hb85xmkKkPYTfZC-ARAkYMcJW-P5K3zvR4MsfNBd8vQBuYse6LgFj4WZ9SRmbduYDPzQT0FFnmm2VT799g1uiP22jbkUjeklxSTGjZbhjn1-8mO1V2gg807Tl6ur54vbtPpw83dxfk0NVzhPOWqKDToojZADVleNyStBa5FZuu6rI0ttJCZEkKhbERJBc8NN6jQki7rnI-T03Xul3ffCwrzqm-Doa7TA7lFqEqZQ44CRCSP_5Gf8ch4dYQKDlCUCBE6W0PGuxA82erLt732ywqhWqmuoupqpbpaq44TJ5tYHaIP66PNNvyNKZVLlKtFj9ZcS0S_bSFAxvX4D_kshqE</recordid><startdate>200801</startdate><enddate>200801</enddate><creator>Jun Guo</creator><creator>Wong, E.W.M.</creator><creator>Chan, S.</creator><creator>Taylor, P.</creator><creator>Zukerman, M.</creator><creator>Kit-Sang Tang</creator><general>IEEE</general><general>Institute of Electrical and Electronic Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>200801</creationdate><title>Performance Analysis of Resource Selection Schemes for a Large Scale Video-on-Demand System</title><author>Jun Guo ; Wong, E.W.M. ; Chan, S. ; Taylor, P. ; Zukerman, M. ; Kit-Sang Tang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c391t-3966a0a6bc0edef3bde7ff03a42fbb8bcf6a472944917d48e635c3c191fea8b53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Applied sciences</topic><topic>Blocking probability</topic><topic>Capacity planning</topic><topic>Computer science; control theory; systems</topic><topic>Computer systems and distributed systems. User interface</topic><topic>Constraint optimization</topic><topic>Design optimization</topic><topic>Disks</topic><topic>Exact sciences and technology</topic><topic>fixed-point approximation</topic><topic>Large-scale systems</topic><topic>Mathematical analysis</topic><topic>Mathematics</topic><topic>Motion pictures</topic><topic>Multimedia</topic><topic>Optimization</topic><topic>Performance analysis</topic><topic>Performance evaluation</topic><topic>resource selection</topic><topic>Servers</topic><topic>Software</topic><topic>Statistics</topic><topic>Tasks</topic><topic>Testing</topic><topic>Video on demand</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jun Guo</creatorcontrib><creatorcontrib>Wong, E.W.M.</creatorcontrib><creatorcontrib>Chan, S.</creatorcontrib><creatorcontrib>Taylor, P.</creatorcontrib><creatorcontrib>Zukerman, M.</creatorcontrib><creatorcontrib>Kit-Sang Tang</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEL</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on multimedia</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jun Guo</au><au>Wong, E.W.M.</au><au>Chan, S.</au><au>Taylor, P.</au><au>Zukerman, M.</au><au>Kit-Sang Tang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Performance Analysis of Resource Selection Schemes for a Large Scale Video-on-Demand System</atitle><jtitle>IEEE transactions on multimedia</jtitle><stitle>TMM</stitle><date>2008-01</date><risdate>2008</risdate><volume>10</volume><issue>1</issue><spage>153</spage><epage>159</epage><pages>153-159</pages><issn>1520-9210</issn><eissn>1941-0077</eissn><coden>ITMUF8</coden><abstract>The designers of a large scale video-on-demand system face an optimization problem of deciding how to assign movies to multiple disks (servers) such that the request blocking probability is minimized subject to capacity constraints. To solve this problem, it is essential to develop scalable and accurate analytical means to evaluate the blocking performance of the system for a given file assignment. The performance analysis is made more complicated by the fact that the request blocking probability depends also on how disks are selected to serve user requests for multicopy movies. In this paper, we analyze several efficient resource selection schemes. Numerical results demonstrate that our analysis is scalable and sufficiently accurate to support the task of file assignment optimization in such a system.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TMM.2007.911281</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1520-9210
ispartof IEEE transactions on multimedia, 2008-01, Vol.10 (1), p.153-159
issn 1520-9210
1941-0077
language eng
recordid cdi_ieee_primary_4407514
source IEEE Xplore (Online service)
subjects Applied sciences
Blocking probability
Capacity planning
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Constraint optimization
Design optimization
Disks
Exact sciences and technology
fixed-point approximation
Large-scale systems
Mathematical analysis
Mathematics
Motion pictures
Multimedia
Optimization
Performance analysis
Performance evaluation
resource selection
Servers
Software
Statistics
Tasks
Testing
Video on demand
title Performance Analysis of Resource Selection Schemes for a Large Scale Video-on-Demand System
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T06%3A45%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Performance%20Analysis%20of%20Resource%20Selection%20Schemes%20for%20a%20Large%20Scale%20Video-on-Demand%20System&rft.jtitle=IEEE%20transactions%20on%20multimedia&rft.au=Jun%20Guo&rft.date=2008-01&rft.volume=10&rft.issue=1&rft.spage=153&rft.epage=159&rft.pages=153-159&rft.issn=1520-9210&rft.eissn=1941-0077&rft.coden=ITMUF8&rft_id=info:doi/10.1109/TMM.2007.911281&rft_dat=%3Cproquest_ieee_%3E2327137511%3C/proquest_ieee_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c391t-3966a0a6bc0edef3bde7ff03a42fbb8bcf6a472944917d48e635c3c191fea8b53%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=863006810&rft_id=info:pmid/&rft_ieee_id=4407514&rfr_iscdi=true