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A Comparative Study for Optimization of Video File Compression in Cloud Environment

by Navdeep S. Chahal, Baljit S. Khehra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 13
Year of Publication: 2012
Authors: Navdeep S. Chahal, Baljit S. Khehra
10.5120/9753-4334

Navdeep S. Chahal, Baljit S. Khehra . A Comparative Study for Optimization of Video File Compression in Cloud Environment. International Journal of Computer Applications. 60, 13 ( December 2012), 27-30. DOI=10.5120/9753-4334

@article{ 10.5120/9753-4334,
author = { Navdeep S. Chahal, Baljit S. Khehra },
title = { A Comparative Study for Optimization of Video File Compression in Cloud Environment },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 13 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 27-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number13/9753-4334/ },
doi = { 10.5120/9753-4334 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:06:29.486048+05:30
%A Navdeep S. Chahal
%A Baljit S. Khehra
%T A Comparative Study for Optimization of Video File Compression in Cloud Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 13
%P 27-30
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Many organizations like hospitals for telemedicine, journalism for live-telecast and academias are using a service video-on-demand for delivering the lectures and research contents to the remote locations across the globe. The videos to be broadcasted are time and resource consuming due to the large amount of data and due to these constraints, for getting fast access over Internet and mobile devices, such video applications need to be compressed into another format. The usage of videos is occasional so to save huge infrastructure cost and time, the Infrastructure as a Service (IaaS) Cloud systems can be leveraged. In this paper, an attempt has been made to design, implement and optimize the performance of Digital Video to MPEG4 transcoding in the Cloud environment using Meghdoot (an Open-Source Cloud stack). The classical MapReduce approach is used to rationalize the use of resources by exploring on demand computing and performs parallel video conversion thereby reducing the video encoding times. Experimental results point out to suitability of better performance that by varying the technique of splitting the video file size of fragments that is through Mencoder and through default Hadoop Splitting. The comparison of both the systems to get the best compression times will help us to optimize the Cloud resources that further helps in trade-off between time, cost and quality.

References
  1. Armbrust, M. , Fox, M. , Griffith, R. , et al. (2009) "Above the Clouds: A Berkeley View of Cloud Computing", In: University of California at Berkeley Technical Report no. UCB/EECS-2009-28, pp. 6-7, February 10, 2009
  2. Huaglory Tianfield, Glasgow Caledonian University, "Cloud Computing Architectures", 978-1-4577-0653-0/11, IEEE 2011
  3. Rafael Pereira, Karin Breitman, " An Architecture for Distributed High Performance Video Processing in the Cloud", 3rd International Conference on Cloud Computing, page 482-489, IEEE 2010
  4. Dean, J. , Ghemawat, S. MapReduce: Simplified Data Processing on Large Clusters. In OSDI, 2004
  5. Converting video formats with FFMpeg, Linux Journal archive- Issue 146, June 2006, pp. 10
  6. Mencoder – http://www. mplayerhq. hu
  7. Apache Hadoop - http://hadoop. apache. org/mapreduce/
  8. Shivnath Babu: Towards automatic optimization of MapReduce programs, In: the 1st ACM symposium on Cloud computing, pp. 137-142, ACM Press, New York (2010)
  9. Daniel Gmach & Ludmila Cherkasova, HP Labs, Palo Alto, CA (USA) and Jerry Rolia HP Labs, Bristol (UK) "Resource and Virtualization Costs up in the Cloud: Models and Design Choices", 978-1-4244-9233-6/11, IEEE 2011
  10. Rakesh Kumar Jha, Upena D Dalal, "On Demand Cloud Computing Performance Analysis With Low Cost For QoS Application", International Conference on Multimedia, IMPACT-2011, 978-1-4577, IEEE 2011
  11. Rafael Silva Pereira, Karin K. Breitman, "Video processing in the Cloud" SpringerBriefs in Computer Science, ISBN: 978-1-4471-2136-7
  12. Jiann-Liang Chen_z, Szu-Lin Wuy, Yanuarius Teofilus Larosa, Pei-Jia Yang and Yang-Fang Li, " IMS Cloud Computing Architecture for High-Quality Multimedia Applications", 978-1-4577-9538, page 1463-1468, IEEE 2011
  13. Adriana Garcia Kunzel , Hari Kalva , Borko Furht, "A Study of Transcoding on Cloud Environments for Video Content Delivery" 978-1-4503-0168/10, page 13-18, ACM 2010
  14. Hui Kang, Yao Chen, Jennifer L. Wong, "Enhancement of Xen's Scheduler for MapReduce Workloads" 978-1-4503-0552-5/11/06, June 8-11, ACM 2011
  15. Dominique A. Heger, "Optimized Resource Allocation & Task Scheduling Challenges in Cloud Computing Environments", dheger@dhtusa. com
  16. Rajkumar Buyya, James Broberg, Andrzej Goscinski, "Cloud Computing, Principles and Paradigms, Wiley 2011, ISBN: 978-0-470-88799-8, page 123-151
  17. Hai Zhong, Kun Tao, Xuejie Zhang, "An Approach to Optimized Resource Scheduling Algorithm for Open-source Cloud Systems", 978-0-7695-4106-8/10, page 124-129, IEEE 2010
  18. Venkatesa Kumar, V. , S. Palaniswami "A Dynamic Resource Allocation Method for Parallel Data Processing in Cloud Computing" ISSN 1549-3636, page 780-788, Journal of Computer Science 8 (5), 2012
Index Terms

Computer Science
Information Sciences

Keywords

Cloud Computing Video Compression Meghdoot MapReduce Hadoop

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