Abstract
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Abstract
Study of Hadoop-based Video Transcoding Optimization
Under circumstances of “Internet Plus”, people have the growing demand for
video transcoding which with higher speed and quality than before. Based on the
report of 2015 Annual Summary for Network Traffic, video created the largest
proportion of the total network traffic consumption. For now, both industry and
academic world are concentrating on how to transcode video efficiently with high
quality and high-availability. This report focused on the research for optimizing video
transcoding process.
To obtain an efficient process and an accurate result, the experiment used cloud
computing method to transcode video. This method could enable the cloud to do the
parallel computing. The report also designed a cloud platform of video transcoding.
This platform used a three-level structure: IaaS, Paas and SaaS. We chose Amazon for
IaaS level, Hadoop for PaaS level and a high performance video transcoding
application for SaaS level. The report also introduced a video transcoding
optimization strategy, which combined the FMPEG and the MapReduce technic.
This report mentioned an architecture called S_MapReduce. Introducing the
virtual IP mechanism into S_MapReduce could improve many performances such as
increasing scalability and making the MapReduce architecture able to cross-domain.
A heart beat process was also designed in the S_MapReduce architecture, which
ameliorated the load –balance module and made the whole architecture more stable
than before. This process enhance the computing architecture performance by
optimizing the MapReduce’s work-flow and data-flow process.
The video transcoding is based on the cloud platform, which combines the
FFMPEG and S_MapReduce to implement the distributed transcoding. The main
process include :(1) According to the fixed scene and several limited conditions,
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