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Load and video performance patterns of a cloud based WebRTC Architecture
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
2017 (English)In: Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 739-744Conference paper, Published paper (Refereed)
Abstract [en]

Web Real-Time Communication or Realtime communication in the Web (WebRTC/RTCWeb) is a prolific new standard and technology stack, providing full audio/video agnostic communications for the Web. Service providers implementing such technology deal with various levels of complexity ranging anywhere from: high service distribution, multi-client integration, P2P and Cloud assisted communication backends, content delivery, real-Time constraints and across clouds resource allocation. This work presents a study of the joint factors including multi-cloud distribution, network performance, media parameters and back-end resource loads, in Cloud based Media Selective Forwarding Units for WebRTC infrastructures. The monitored workload is sampled from a large population of real users of our testing infrastructure, additionally the performance data is sampled both by passive user measurements as well as server side measurements. Patterns correlating such factors enable designing adaptive resource allocation algorithms and defining media Service Level Objectives (SLO) spanning over multiple data-centers or servers. Based on our analysis, we discover strong periodical load patterns even though the nature of user interaction with the system is mostly not predetermined with variable user churn.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2017. p. 739-744
Keywords [en]
bitrate, load measurements, media, rtp/rtcp, stream allocation, webrtc, Cluster computing, Grid computing, Population statistics, Resource allocation, Systems analysis, Bit rates, Distributed computer systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-216290DOI: 10.1109/CCGRID.2017.118ISI: 2-s2.0-85027437231Scopus ID: 2-s2.0-85027437231ISBN: 9781509066100 OAI: oai:DiVA.org:kth-216290DiVA, id: diva2:1164588
Conference
17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, 14 May 2017 through 17 May 2017
Note

QC 20171211

Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2018-04-03Bibliographically approved

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Vlassov, Vladimir

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf