CVSHR: Enchantment Cloud-based Video Streaming using the Heterogeneous Resource Allocation

Автор: Mohamed A. Elsharkawey, Hosam E. Refaat

Журнал: International Journal of Computer Network and Information Security(IJCNIS) @ijcnis

Статья в выпуске: 9 vol.9, 2017 года.

Бесплатный доступ

The Video requests can be streamed in two forms. They are the live streaming and the on-demand streaming. Both of them should be adapted (I.e., transcoded) to fit the characteristics (e.g., spatial resolution, bit rate… and the supported formats) of client devices. Therefore, many streaming service providers are presented the cloud services to be utilized in the video transcoding. But, the introducing of the cloud services for video transcoding is encountered by the contradiction between the deploying cloud resources in a cost-ef?cient without any major influence on the quality of video streams. In order to address this problem, this paper presents an Enchantment Cloud-based Video Streaming using the Heterogeneous Resource Allocation (CVSHR) to transcode the video streams on cloud resources in an efficient manner with the QoS of the requested video stream. The system architecture is elastic and based on multiple heterogeneous clusters that provide a great flexible resource allocation and De-allocation strategy. This strategy aims to assign a suitable VM with adequate resources based on the GOPs characteristic. Also, it can reassign the unused resources. In addition, the number of VMs can be extended as the system necessity. Finally, The CVSHR is simulated and evaluated on truthful cloud resources and various workload circumstances.

Еще

Cloud system, video streams, elastic resource allocation, QoS

Короткий адрес: https://sciup.org/15011883

IDR: 15011883

Список литературы CVSHR: Enchantment Cloud-based Video Streaming using the Heterogeneous Resource Allocation

  • C. V. N. Index, “Forecast and methodology, 2014-2019,” 2015.
  • I. Ahmad, X. Wei, Y. Sun, and Y.-Q. Zhang, “Video transcoding: an overview of various techniques and research issues,” IEEE Transactionson Multimedia, vol. 7, no. 5, pp. 793–804, 2005.
  • Zhijun Lei and Nicolas D. GeorganasAdaptive video transcoding and streaming over wireless channels Journal of Systems and Software, Volume 75, Issue 3, pp 253-270, March 2005.
  • X. Li, M. A. Salehi, and M. Bayoumi, “Cloud-based video streaming for energy- and compute-limited thin clients,” in the Stream2015 Workshopat Indiana University, Oct, 2015.
  • Khosro Mogouie, Mostafa Ghobaei Arani, Mahboubeh Shamsi, “A Novel Approach for Optimization Auto-Scaling in Cloud Computing Environment”, PP.46-53, Pub. Date: 2015-10-8, DOI: 10.5815/ijcnis.2015.11.05
  • Xiangbo Li, Mohsen AminiSalehi, Magdy Bayoumi, Rajkumar Buyya, "CVSS: A Cost-Ef?cient and QoS-Aware Video Streaming Using Cloud Services", in 16th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, 2016.
  • M. A. Mesa, A. Ramirez, A. Azevedo, C. Meenderinck, B. Juurlink, and M. Valero, “Scalability of macroblock-level parallelism for h. 264 decoding,” in Proceedings of the 15th IEEE International Conference on Parallel and Distributed Systems (ICPADS), pp. 236–243, 2009.
  • F. Lao, X. Zhang, and Z. Guo, “Parallelizing video transcoding using map-reduce-based cloud computing,” in Proceedings of IEEE InternationalSymposium on Circuits and Systems, pp. 2905–2908, 2012.
  • F. Jokhio, T. Deneke, S. Lafond, and J. Lilius, “Analysis of video segmentation for spatial resolution reduction video transcoding,” in Proceedings of IEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), pp. 1–6, 2011.
  • A. Vetro, C. Christopoulos, and H. Sun, “Video transcoding architectures and techniques: an overview,” IEEE on Signal ProcessingMagazine, vol. 20, no. 2, pp. 18–29, 2003.
  • ZhijunLei, and Nicolas D.Georganas "A rate adaptation transcoding scheme for real-time video transmission over wireless channels" Signal Processing: Image Communication, Volume 18, Issue 8, pp 641-658, September 2003.
  • J. Xin, M.-T. Sun, K. Chun, and B. S. Choi, “Motion re-estimation for hdtv to sdtv transcoding,” in Proceedings of IEEE InternationalSymposium on Circuits and Systems (ISCAS), vol. 4, pp. IV–715, 2002.
  • F. Jokhio, A. Ashraf, S. Lafond, and J. Lilius, “A computation and storage trade-off strategy for cost-efficient video transcoding in the cloud,” in Proceedings of the 39th IEEE Conference on Software Engineering and Advanced Applications (SEAA), pp. 365–372, 2013.
  • Y. Ismail, J. B. McNeely, M. Shaaban, H. Mahmoud, M. Bayoumiet al., “Fast motion estimation system using dynamic models for h. 264/avcvideo coding,” IEEE Transactions on Circuits and Systems for VideoTechnology, vol. 22, no. 1, pp. 28–42, 2012.
  • T. Shanableh, E. Peixoto, and E. Izquierdo, “Mpeg-2 to hevc video transcoding with content-based modeling”,IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, pp. 1191–1196, 2013.
  • Deepika Saxena, R.K. Chauhan, Ramesh Kait "Dynamic Fair Priority Optimization Task Scheduling Algorithm in Cloud Computing: Concepts and Implementations", PP.41-48, Pub. Date: 2016-2-8, DOI: 10.5815/ijcnis.2016.02.05
  • Mokhtar A. Alworafi, Atyaf Dhari, Asma A. Al-Hashmi, Suresha, A. Basit Darem "Cost-Aware Task Scheduling in Cloud Computing Environment", PP.52-59, Pub. Date: 2017-5-8, DOI: 10.5815/ijcnis.2017.05.07
  • W. Chen and E. Deelman, “Workflowsim: A toolkit for simulating scientific workflows in distributed environments,” in 2012 IEEE 8th International Conference on E-Science, ser. eScience, 2012, pp. 1–8. [Online]. Available:https://github.com/WorkflowSim
  • R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software: Practice and Experience, vol. 41, no. 1, 2011.
Еще
Статья научная