Vol. 2 Issue 2
Year: 2015
Issue:Feb-Apr
Title:Scalable Video Transcoding with Hadoop MapReduce in Openstack Juno Platform
Author Name:D. Kesavaraja and A.Shenbagavalli
Synopsis:
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).
Year: 2015
Issue:Feb-Apr
Title:Scalable Video Transcoding with Hadoop MapReduce in Openstack Juno Platform
Author Name:D. Kesavaraja and A.Shenbagavalli
Synopsis:
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).
No comments:
Post a Comment