Architecture for Predicting Live Video Transcoding Performance on Docker Containers

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)

Abstract

Video can be streamed live with differentapplications (e.g. YouTube Live, Periscope). Typically, thevideo content is adapted for end users based on receivingclient’s capabilities, and network bandwidth. The adaptation isrealized with different video representations, which are createdby transcoding the original video content. When video isstreamed live, transcoding has to be completed within real timeconstraints, which is a computationally demanding process.Particularly, live transcoding should be enabled efficiently by acontent distributor to minimize resource provisioning costs.The contribution of this paper is an architecture for predictinglive video transcoding performance on a Docker-basedplatform. Particularly, cloud resource management for livevideo transcoding has been focused on. A model was trainedbased on measurements in different transcodingconfigurations. Offline evaluation results indicate that livetranscoding speed or CPU usage can be predicted with 3-8 %accuracy. When video is transcoded on virtual machines basedon predictions in a prototype system (live), live transcodingspeed prediction accuracy is within a similar range as theoffline performance, but worse for CPU usage prediction (5-15%). In most cases the specified range for transcoding speedand CPU usage can be achieved at least with a precision of 76%.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Services Computing, SCC 2018
Subtitle of host publicationPart of the 2018 IEEE World Congress on Services
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages65-72
Number of pages8
ISBN (Electronic)978-1-5386-7250-1
ISBN (Print)978-1-5386-7251-8
DOIs
Publication statusPublished - 1 Jul 2018
MoE publication typeNot Eligible
EventIEEE International Conference on Services Computing, SCC 2018 - San Francisco, United States
Duration: 2 Jul 20187 Jul 2018

Conference

ConferenceIEEE International Conference on Services Computing, SCC 2018
Abbreviated titleSCC 2018
CountryUnited States
CitySan Francisco
Period2/07/187/07/18

Fingerprint

Program processors
Containers
Bandwidth
Costs
Virtual machine

Keywords

  • video transcoding
  • FFmpeg
  • Rancher
  • Cassandra
  • Docker
  • Random forest
  • Prometheus

Cite this

Pääkkönen, P., Heikkinen, A., & Aihkisalo, T. (2018). Architecture for Predicting Live Video Transcoding Performance on Docker Containers. In 2018 IEEE International Conference on Services Computing, SCC 2018: Part of the 2018 IEEE World Congress on Services (pp. 65-72). [8456402] Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/SCC.2018.00016
Pääkkönen, Pekka ; Heikkinen, Antti ; Aihkisalo, Tommi. / Architecture for Predicting Live Video Transcoding Performance on Docker Containers. 2018 IEEE International Conference on Services Computing, SCC 2018: Part of the 2018 IEEE World Congress on Services. Institute of Electrical and Electronic Engineers IEEE, 2018. pp. 65-72
@inproceedings{ebac1b68b55f47588e011a6a241d09ce,
title = "Architecture for Predicting Live Video Transcoding Performance on Docker Containers",
abstract = "Video can be streamed live with differentapplications (e.g. YouTube Live, Periscope). Typically, thevideo content is adapted for end users based on receivingclient’s capabilities, and network bandwidth. The adaptation isrealized with different video representations, which are createdby transcoding the original video content. When video isstreamed live, transcoding has to be completed within real timeconstraints, which is a computationally demanding process.Particularly, live transcoding should be enabled efficiently by acontent distributor to minimize resource provisioning costs.The contribution of this paper is an architecture for predictinglive video transcoding performance on a Docker-basedplatform. Particularly, cloud resource management for livevideo transcoding has been focused on. A model was trainedbased on measurements in different transcodingconfigurations. Offline evaluation results indicate that livetranscoding speed or CPU usage can be predicted with 3-8 {\%}accuracy. When video is transcoded on virtual machines basedon predictions in a prototype system (live), live transcodingspeed prediction accuracy is within a similar range as theoffline performance, but worse for CPU usage prediction (5-15{\%}). In most cases the specified range for transcoding speedand CPU usage can be achieved at least with a precision of 76{\%}.",
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Pääkkönen, P, Heikkinen, A & Aihkisalo, T 2018, Architecture for Predicting Live Video Transcoding Performance on Docker Containers. in 2018 IEEE International Conference on Services Computing, SCC 2018: Part of the 2018 IEEE World Congress on Services., 8456402, Institute of Electrical and Electronic Engineers IEEE, pp. 65-72, IEEE International Conference on Services Computing, SCC 2018, San Francisco, United States, 2/07/18. https://doi.org/10.1109/SCC.2018.00016

Architecture for Predicting Live Video Transcoding Performance on Docker Containers. / Pääkkönen, Pekka; Heikkinen, Antti; Aihkisalo, Tommi.

2018 IEEE International Conference on Services Computing, SCC 2018: Part of the 2018 IEEE World Congress on Services. Institute of Electrical and Electronic Engineers IEEE, 2018. p. 65-72 8456402.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Pääkkönen P, Heikkinen A, Aihkisalo T. Architecture for Predicting Live Video Transcoding Performance on Docker Containers. In 2018 IEEE International Conference on Services Computing, SCC 2018: Part of the 2018 IEEE World Congress on Services. Institute of Electrical and Electronic Engineers IEEE. 2018. p. 65-72. 8456402 https://doi.org/10.1109/SCC.2018.00016