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Complexity of cloud-based transcoding platform for scalable and effective video streaming services

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Abstract

Nowadays, a fast network improves the quality of our daily life and we can enjoy a variety of services over the Internet. Different types of media streaming services have been proposed and utilized as the network speed is now sufficiently fast to deliver high-quality live streaming. Usually, different media streaming services deliver streaming data by using different protocols such as the real-time message protocol (RTMP), real-time streaming protocol (RTSP), and Windows media HTTP streaming protocol (WMSP). In this paper, we propose and implement a cloud-based scalable and cost-effective video streaming transcoding service platform to provide the service of changing real-time streaming protocols (RTMP/RTSP) and codecs (H.263/H.264). A transcoder dispatching problem (TDP) over the cloud platform is also defined, which attempts to serve all the transcoding requests by minimizing the cost of virtual machines. Further, a transcoder dispatching algorithm and an online transcoder dispatching algorithm are proposed for the TDP. These algorithms are implemented on the Amazon EC2 platform. Experimental results demonstrate that by renting different levels of virtual machines dynamically and intelligently, we can provide a scalable and cost-effective transcoding service for bridging heterogeneous streaming media.

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References

  1. Amazon EC2, http://aws.amazon.com/ec2/

  2. Apple, http://www.apple.com/

  3. Armbrust M et al (2009) A view of cloud computing. Commun ACM 53(4):50–58

    Article  Google Scholar 

  4. Assuncao MDD et al (2009) Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters. Proceedings of the 18th ACM International Symposium on High Performance Distributed Computing. Garching, Germany, 141–150

  5. Bi J et al (2010) Dynamic provisioning modeling for virtualized multi-tier applications in cloud data center. Proceedings of the 3rd International Conference on Cloud Computing (CLOUD’10), Miami, FL, 5–10 July, 370–377

  6. Chieu TC et al (009) Dynamic scaling of web applications in a virtualized cloud computing environment. Proceedings of the 6th International Conference on e-Business Engineering (ICEBE’09), Macau, 21–23 Oct., 281–286

  7. Chunghwa Telecom, http://www.cht.com.tw/

  8. FFmpeg, http://www.ffmpeg.org/

  9. Google App Engine, http://code.google.com/intl/zh-TW/appengine/

  10. H.264 AVC, http://en.wikipedia.org/wiki/H.264/MPEG-4_AVC

  11. Lee YC et al (2010) Profit-driven service request scheduling in clouds. 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), Melbourne, Australia, 17–20 May, 15–24.

  12. Mao M et al (2010) Cloud auto-scaling with deadline and budget constraints. 11th IEEE/ACM International Conference on Grid Computing (GRID), Brussels, 25–28 Oct. 41–48

  13. Marshall P et al (2010) Elastic site: Using clouds to elastically extend site resources. 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), Melbourne, Australia, 17–20 May, 43–52

  14. Microsoft Windows Azure, http://www.microsoft.com/windowsazure/

  15. Pishinger D (1995) A minimal algorithm for the bound knapsack problem. In: Balas E, Clausen J (eds) Integer programming and combinatorial optimization, four OPCO conference. Lecture notes in computer science, 920. Springer, Berlin

    Google Scholar 

  16. Rombaut, et al (2009) xStreamer: Modular Multimedia Streaming. Proc. 17th ACM Int. Conf. Multimedia, 929–930.

  17. RTMP, https://www.adobe.com/devnet/rtmp.html

  18. RTSP, https://www.ietf.org/rfc/rfc2326.txt

  19. ShareRoom, http://www.netxtream.com

  20. Sirannon, http://sirannon.atlantis.ugent.be/

  21. VLC, http://www.videolan.org/vlc/

  22. Wowza, http://www.wowzamedia.com

  23. Zhu Q and Agrawal G (2010) Resource provisioning with budget constraints for adaptive applications in cloud environments. Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing (HPDC’10), 304–307

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Acknowledgments

This work was supported by Ministry of Science and Technology (MOST) project of Taiwan [MOST 103-2221-E-415-021-] and [MOST 104-2221-E-415-003-].

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Minghsin University of Science and Technology, Hsinchu County, Taiwan

    Kwei-Bor Chen

  2. Department of Management Information Systems, National Chiayi University, Chiayi, Taiwan

    Hong-Yi Chang

Authors
  1. Kwei-Bor Chen
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  2. Hong-Yi Chang
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Correspondence to Hong-Yi Chang.

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Chen, KB., Chang, HY. Complexity of cloud-based transcoding platform for scalable and effective video streaming services. Multimed Tools Appl 76, 19557–19574 (2017). https://doi.org/10.1007/s11042-016-3247-z

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  • DOI: https://doi.org/10.1007/s11042-016-3247-z

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