dynamic resource allocation and distributed video transcoding in cloud computing
TRANSCRIPT
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Optimization of distributed
video transcoding using mapreduce and dynamic resource
allocation in cloud computing
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ABSTRACT
In this paper, we propose a HadoopMap Reduce based Distributed Video
Transcoding System in a cloud
computing environment thattranscodes various video codec
formats into the MPEG-4 video format.
We will optimize the input files andparallely split the file to store in cloud
server.
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Continued
The users may access the computingresources by using computer, tablet,
notebook, smartphone, pad computer
or other devices. The cloud server provides and
manages the applications and also
storing the data remotely in cloud.
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Thus, the encoding time to transcode
large amounts of video content is
exponentially reduced, facilitating atranscoding function.
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Abstract- continued
For performance evaluation, we focuson measuring the total time to
transcode a data set into a target data
set for three sets of experiments. Wehave proposed the solution that how
video transcoding becomes smart and
speeds-up due to the efficiency ofcloud computing.
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Introduction
Cloud computing is an Internet basedservices, where we share some of the
services like software, platform,
infrastructure, storage, databases tocomputer or other devices on demand
by the users.
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Services are sold on demand, for aminute/hourly basis, services are fully
managed by the providers and
consumer need only is a computerand Internet access.
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Existing System
Hadoop-based Distributed VideoTranscoding System in a cloudcomputing environment that transcodesvarious video codec formats into theMPEG-4 video format.
This system provides various types ofvideo content to heterogeneous devices
such as smart phones, personalcomputers, television, and pads.
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We design and implement the systemusing the MapReduce framework,
which runs on a Hadoop Distributed
File System platform, and the mediaprocessing library Xuggler.
Thus, the encoding time to transcode
large amounts of video content isexponentially reduced, facilitating a
transcoding function.
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For performance evaluation, we focuson measuring the total time to
transcode a data set into a target data
set for three sets of experiments. We also analyze the experimental
results, providing optimal Hadoop
Distributed File System andMapReduce options suitable for video
transcoding.
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Disadvantages:
There are no optimization techniques
available for cloud resource.
Transcoding is not effective.
It will take more time for encoding the
file.
Performance is very less.
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Proposed System:
In this paper, we propose a Hadoop MapReduce based Distributed VideoTranscoding System in a cloudcomputing environment that transcodes
various video codec formats into theMPEG-4 video format.
The users may access the computing
resources by using computer, tablet,notebook, smartphone, pad computer orother devices.
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The cloud server provides and managesthe applications and also storing the dataremotely in cloud.
For performance evaluation, we focus onmeasuring the total time to transcode adata set into a target data set for threesets of experiments.
We have proposed the solution that howvideo transcoding becomes smart andspeeds-up due to the efficiency of cloudcomputing.
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Advantages:
Optimization techniques available for
cloud resource.
Transcoding is effective. It is smart and efficient for transcoding the
file.
Performance is high.
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Hadoop Map Reduce
Technique Hadoop MapReduce (Hadoop
Map/Reduce) is a software frameworkfor distributed processing of large datasets on compute clusters of commodity
hardware. The framework takes care of scheduling
tasks, monitoring them and re-executing
any failed tasks. The primary objective of Map/Reduce is
to split the input data set intoindependent chunks that are processed
in a completely parallel manner.
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The Hadoop MapReduce frameworksorts the outputs of the maps, which
are then input to the reduce tasks.
Typically, both the input and the outputof the job are stored in a file system.
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System Architecture
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Conclusion
We proposed a Hadoop Map Reducebased Distributed Video Transcoding
System in a cloud computing
environment that transcodes variousvideo codec formats into the MPEG-4
video format.
Our system ensures uniform transcoded
video quality and a fast transcoding
process by applying HDFS and
MapReduce, the core techniques in
cloud computing enabling technologies.
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We can further optimize these splits,analyzing what is the optimum amount ofchunks to be generated, which certainlyvary according to the different data types
(text, images, etc). Performance of Hadoop MapReducejobs can be improved without increasing
hardware costs, by tuning several keyconfiguration parameters for clusterspecifications, input data size andprocessing complexity.
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Lot of research work is still going on tooptimize the resources of Cloud
computing based upon scheduling,
elasticity and scalability. Future work includes the experiments
with public Cloud and with different set
of inputs.