dynamic resource allocation and distributed video transcoding in cloud computing

7/29/2019 dynamic resource allocation and distributed video transcoding in cloud computing

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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.

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dynamic resource allocation and distributed video transcoding in cloud computing