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http://hdl.handle.net/2080/2722
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DC Field | Value | Language |
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dc.contributor.author | Sahoo, Sampa | - |
dc.contributor.author | Parida, Ipsita | - |
dc.contributor.author | Mishra, Sambit Kumar | - |
dc.contributor.author | Sahoo, Bibhdatta | - |
dc.contributor.author | Turuk, Ashok Kumar | - |
dc.date.accessioned | 2017-07-03T11:50:56Z | - |
dc.date.available | 2017-07-03T11:50:56Z | - |
dc.date.issued | 2017-06 | - |
dc.identifier.citation | 5th International Conference on Advanced Computing, Networking, and Informatics(ICACNI), NIT Goa, India,1-3 June 2017 | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/2722 | - |
dc.description | Copyright for this paper belongs to proceeding publisher | en_US |
dc.description.abstract | Video content providers like YouTube, Netix cater their content, i.e. news, shows, on the web which is accessible anytime anywhere. The multi-screens like TVs, smartphones, laptops created a demand to transcode the video into the appropriate video speciation ensuring different Quality of Services (QoS) such as delay. Transcoding a large, high-definition video requires a lot of time, computation. The cloud transcoding solution allows video service providers to overcome the above difficulties through the pay-as-you-use scheme, with the assurance of providing online support to handle unpredictable demands. This paper presents a cost-efficient cloud-based transcoding framework and algorithm (CVS) for streaming service providers. The dynamic resource provisioning policy used in framework finds the number of virtual ma-chines required for a particular set of video streams. Simulation results based on YouTube dataset show that the CVS algorithm performs better compared to FCFS scheme. | en_US |
dc.publisher | Springer | en_US |
dc.subject | Cloud | en_US |
dc.subject | Video transcoding | en_US |
dc.subject | Merger | en_US |
dc.subject | Video Stream | en_US |
dc.subject | QoS | en_US |
dc.title | Resource Allocation for Video Transcoding in the Multimedia Cloud | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2017_ICACCNI_SSahoo_Resource.pdf | 318.3 kB | Adobe PDF | View/Open |
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