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Scalable Video Transcoding with Hadoop MapReduce in Openstack Juno Platform

D. Kesavaraja*, A.Shenbagavalli**

* Assistant Professor, Department of Computer Science and Engineering, Dr. Sivanthi Aditanar College of Engineering, Tiruchendur, India.

** Professor & Head, Department of Electronics and Communication Engineering, National Engineering College, Kovilpatti, India.

Periodicity:February - April'2015
DOI : https://doi.org/10.26634/jcc.2.2.3447

Abstract

Cloud computing and big data are changing today’s modern on demand video service.This paper describes how to increase the speed of video transcoding in an open stack private cloud environment using Hadoop Map Reduce. In this paper, OpenStack Juno is used to build the private cloud infrastructure as a service having map code executing on the node, where the video transcoding resides, to significantly reduce this problem. This practice, called “video locality”, is one of the key advantages of Hadoop MapReduce. This scheme describes the deep relationship of a Hadoop Map Reduce algorithm and video transcoding in the experiment. As a result of Map Reduce video transcoding experiment in openstack Juno, outstanding performance of the physical server was observed when running on the virtual machine in the private cloud based on the metrics, in terms of Time Complexity and Quality Check using PSNR (Peak Signal-to-Noise Ratio).

Keywords

Cloud Computing, Video Transcoding, Openstack, Hadoop, Map Reduce.

How to Cite this Article?

Kesavaraja, D., and Shenbagavalli, A. (2015). Scalable Video Transcoding with Hadoop Map Reduce in Openstack Juno Platform. i-manager’s Journal on Cloud Computing, 2(2), 14-21. https://doi.org/10.26634/jcc.2.2.3447

References

[1]. Steven C. Markey (2012). "Deploy an OpenStack private cloud to a Hadoop MapReduce environment”, IBM Developer Works.

[2]. [Online] Available :https://www.mirantis.com/blog/ improving-data-processing-per formance-hadoopdata- locality/

[3]. Andrew Lazarev (2014). "Performance of Hadoop on OpenStack", Mirantis [Onine] Available: https:// www.openstack.org/assets/presentation-media/ Performance-of-Hadoop-on-OpenStack.pdf

[4]. G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels, (2007). “Dynamo: Amazon's highly available key-value store,” in SOSP, pp. 205-220.

[5]. D. Beaver, S. Kumar, H. C. Li, J. Sobel, P. Vajgel, (2010). “Finding a needle in haystack: Facebook's photo storage.” in OSDI

[6]. [Online] Available http://docs.openstack.org/ developer/swift/.

[7]. J. Dean and S. Ghemawat, (2004). “Mapreduce: Simplified data processing on large clusters,” in OSD

[8].[Online]Available“Apache HDFS,” http:// hadoop.apache.org/docs/r1.2.1/hdfs design. html.

[9]. [Online] Available “Apache HadoopMapReduce,” http://hadoop.apache.org/.

[10]. [Online] Available “HadoopMapReduce - Swift Connector,” http://goo.gl/mJIT7Y.

[11]. [Online] Available “HiBench,” https://github.com/ intel-hadoop/HiBench.

[12]. M. Mihailescu, G. Soundararajan, and C. Amza, (2013).“Mixapart: decoupled analytics for shared storage systems.” in FAST

[13]. S. Sehrish, G. Mackey, J. Wang, and J. Bent, (2010). “Mrap: a novel Mapreduce based framework to support HPC analytics applications with access patterns,” in HPDC

[14]. Lukas Rupprecht, Rui Zhang , Dean Hildebrand,“Big Data Analytics on Object Stores: A Performance Study” , The International Conference for High Performance Computing, Networking, Storage and Analysis.

[15]. M. Armbrust, A. Fox, R. Griggith, (2009). "Above the cloud: A Berkeley View of Cloud Computing," Technical Report No.UCB/EECS-2009-28, EECS Department, University of California at Berkeley, USA

[16]. Yunhee Kang, Geoffrey C. Fox, (2011). “Performance Evaluation of MapReduce Applications on Cloud Computing Environment, FutureGrid”, Grid and Distributed Computing: Vol. 261, pp. 77-86.

[17]. J. Dean and S. Ghemawat, (2010). "MapReduce: A Flexible Data Processing Tool," Communications of the ACM, Vol. 53, pp. 72-77

[18]. J. Ekanayake, (2008). "MapReduce for Data Intensive Scientific Analyses," the Fourth IEEE International Conference on eScience, pp. 277-284.

[19]. [Online] Available Hadoop. http://hadoop. apache.org/

[20]. [Online] Available OpenStack http://www.openstack.org/

[21]. Yunhee Kang, Kyung-Woo Kang (2013).“An Empirical Study of Hadoop Application running on Private Cloud Environment”, Advanced Science and Technology Letters, Vol. 35(Cloud and Super Computing), pp.70-73.

[22]. [Online] Available http://www.webopedia.com/ TERM/O/openstack-juno.html

[23]. [Online] Available https://www.openstack.org/ software/juno/

[24]. [Online] Available https://en.wikipedia.org/ wiki/OpenStack

Scalable Video Transcoding with Hadoop MapReduce in Openstack Juno Platform

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