Resource Allocation for Video Transcoding in the Multimedia Cloud

被引：1
|
作者：
Sahoo, Sampa [1 ]
Parida, Ipsita [1 ]
Mishra, Sambit Kumar [1 ]
Sahoo, Bibhdatta [1 ]
Turuk, Ashok Kumar [1 ]
机构：
[1] Natl Inst Technol, Rourkela, India
来源：
RECENT FINDINGS IN INTELLIGENT COMPUTING TECHNIQUES, VOL 1 | 2019年 / 707卷
关键词：
Cloud; Video transcoding; Merger; Video stream; QoS;
D O I：
10.1007/978-981-10-8639-7_55
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Video content providers like YouTube and Netflix cater their content, i.e., news and shows, on the web which is accessible anytime anywhere. The multi-screens like TVs, smartphones, and laptops created a demand to transcode the video into the appropriate video specification 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 machines 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.
引用
收藏
页码：525 / 532
页数：8
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