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Cost-Efficient Dynamically Scalable Video Transcoding in Cloud Computing

Fareed Jokhio, Adnan Ashraf, S├ębastien Lafond, Ivan Porres, Johan Lilius, Cost-Efficient Dynamically Scalable Video Transcoding in Cloud Computing. TUCS Technical Reports 1098, TUCS, 2013.


Video transcoding of a large number of on-demand videos requires a large scale cluster of transcoding servers. Moreover, storage of multiple transcoded versions of each source video requires a large amount of disk space. Infrastructure as a Service (IaaS) clouds provide virtual machines (VMs) for creating a dynamically scalable cluster of servers. Likewise, a cloud storage service may be used to store a large number of transcoded videos. Moreover, it may be possible to reduce the total IaaS cost by trading storage for computation, or vice versa. In this paper, we present prediction-based dynamic resource allocation algorithms to scale on-demand video transcoding service on a given IaaS cloud. The proposed algorithms provide mechanisms for allocation and deallocation of VMs to a dynamically scalable cluster of video transcoding servers in a horizontal fashion. We also present a computation and storage trade-off strategy for cost-efficient video transcoding in the cloud called cost and popularity score based strategy. The proposed strategy estimates computation cost, storage cost, and video popularity of individual transcoded videos and then uses this information to make decisions on how long a video should be stored or how frequently it should be re-transcoded from a given source video. The proposed algorithms and the trade-off strategy are demonstrated in a discrete-event simulation and are empirically evaluated using a realistic load pattern.


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BibTeX entry:

  title = {Cost-Efficient Dynamically Scalable Video Transcoding in Cloud Computing},
  author = {Jokhio, Fareed and Ashraf, Adnan and Lafond, S├ębastien and Porres, Ivan and Lilius, Johan},
  number = {1098},
  series = {TUCS Technical Reports},
  publisher = {TUCS},
  year = {2013},
  ISBN = {978-952-12-3001-1},

Belongs to TUCS Research Unit(s): Embedded Systems Laboratory (ESLAB), Software Engineering Laboratory (SE Lab)

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