cloud based e-learning platform using dynamic chunk size

8/22/2019 Cloud Based E-Learning Platform Using Dynamic Chunk Size

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I nternati onal Jour nal of Engineeri ng Trends and Technology- Volume4Issue3- 2013

ISSN: 2231-5381 http://www.internationaljournalssrg.org Page 406

Cloud Based E-Learning Platform Using

Dynamic Chunk Size

Dinoop M.S#1

, Durga .S*2

PG Scholar, Karunya University

Assistant Professor, Karunya University

Abstract: E-learning is a tool which has the

potential to enhance and support the traditional

education method. This paper present and design

the novel approach for the e-learning video on

demand service using the new innovating

technology, cloud computing. For providing thefast down loading and uploading of videos, high

security to the data, less consumption of band

width proposed approach using the dynamic

chunk size method.

I .INTRODUCTION

Structured learning[1] that is carried out

over an electronic platform is called e-learning. E-

learning services can be divided in to Synchronous

and Asynchronous e-learning services[2]. In

synchronous e-learning system the students need to

be online at predefined time. Asynchronous e-

learning service can be accessed by the students

whenever they want. Four main components needed

for effective e-learning system are the participants,

facilitator, course design and technology support. E-

learning service is provided for complete training or

to provide just in time information and expert

guidance. This paper describes a synchronous e-

learning system in which the students must be present

at the class time to get the streaming video tutorial

online.[3]Video on demand is one of the

advancements in the area of multimedia service usingwhich user can select and view the selected video.

Examples of applications of video on demand are

movies on Demand, E-Ecommerce, Interactive

advertisement etc. there are three types of playing

methods. They are download mode, streaming and

progressive download or pseudo streaming. In

download mode the downloaded video is played only

after the complete download of the video. In

Streaming mode the video is downloaded in parts

andeach part is decoded and played before the

complete video is downloaded. In progressive

download the video is downloaded as in download

mode but the user can play the video if the download

speed is sufficiently greater than the playingrate.Main

issues in video on demand service are providing

sufficient bandwidth with low cost and security of

video content[4].There are many technologies that

can be used to provide e-learning video on demand

through internet. They can be traditional web based

technology, grid technology and cloud technology.

Connecting and integrating ideal system resources

with the application of proper operating system and

software are called grid computing technology. By

using this technique collection of sufficient

computing power for a super computer is generated.[5]Providing computational power to a remote place

through a tcp/ip network such as internet is called

cloud computing. The main features of cloud

computing are location independent access,

competitively low deployment cost, scalability etc.

So this provides an effective system for providing e-

learning video on demand.

The challenges in providing an e-learning

system are Timing, Security, Bandwidth, Storage,

Quality of service etc. As explained in a synchronous

video on demand learning system the timing of

students and staffs are important. Security of video

content is important to avoid piracy. Sufficient

bandwidth is needed for an e-learning system. The

problem is we should expect scalability as the

number of students may ramp up any time.

Application of cloud technology can reduce this

problem. Storage space can be reduced by light
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weight storage and using efficient video compression

technique. The quality of service can be reduced by

the usage of Content Delivery Network (CDN).

The novel approach is based on the

progressive -learning , here we introduce the cloud-

based e-learning for the increase of performance,security ,and less bandwidth consumption.

II. RELATED WORKS

There are mainly three types of well-known

web based approaches. They are multi thread based,

RMI based and service oriented based. In 2011

DimitrisKarakasilisand et al.[6] proposed a method in

which python threads running over the server carries

out the functions of the video on demand system. We

can use a remote procedure from a remote place to

activate the video streaming.

P.Seethalakshmi and et al [7].proposed an

RMI based method. The clients can access the media

server at any time they want. Then a multimedia

environment is created by the server. This is done by

summing up the contents for each request and

starting a new thread for each of different content.

Valerie monfort and et al. proposed [8]a

service oriented architecture for e-learning. This

created interoperability between remote and local

homogenous and heterogeneous applications by usingreusable service logic. This provides a

standardization.

Kong Feng and et al. and Chao Tung and et

al. proposed[9] two different architectures for grid

based e-learning video on demand methods. This

method reduces the draw back in traditional web

based approach that is a highly loaded server. This

uses data grid and computational grid to fulfil the

needs for video on demand systems. This technology

integrates many video on demand nodes which solves

the storage as well as the computational needs of the

system. Information service, file management,

resource scheduling, grid portal and content storage

are different components used in these systems to

provide a video on demand distribution algorithm.

The advantages of the system are efficient usage of

resources and delivery of high computational power

and storage. The main disadvantages of these systems

are there are no interactive job submission and grid

software standards are still evolving.

III. PROPOSED WORK

Novel approach is brings the traditional way

of learning in the new world in the new manner withthe help of cloud computing. The novel approach is

based on the progressive e-learning ,here we

introduce the cloud-based e-learning for the increase

of performance ,security ,and less bandwidth

consumption.

The performance cloud based e-learning

video on demand system can be increased by two

techniques. First Adaptive chunk size[10] and Second

Applying a light weight cloud storage engine. From

the performance analyses it is proved that if the

chunk size is increased then the upload time of thevideo is decreased. So instead of using a fixed chunk

size we can calculate the server load and adaptively

change the chunk size. If the server load is high then

the chunk size is decreased and also if the server load

is low then the chunk size is increased.

Video capture

Video capture is the first step in the e-

learning. High definition quality cameras are used to

capture the sound and video. Thus they create a

virtual class room. The class room may be anywherein the world, the users of e-learning only require the

browser which support the internet connection. The

videos can upload only the register members of

cloud. In order to ensure the security and access

control to the data, clouds provide the biometric

authentication mechanism to the users. The following

diagram explains how the users can upload their data

in to cloud.


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Fig 1: Basic credential diagram

For uploading data to the cloud first the

user registers their details in to the cloud and obtains

the credentials for the further usage. Then the cloud

provider is stored the values on the database. When

the users want to upload the data in to the cloud first

they submit the value to the database. The cloud

provider is verifying that whether they submitted

value and the derived value are equal. If both the

values are equal then they allow uploading the value

in to the database.

Uploading

Video uploading can be done using the

cloud system. To provide the efficient storage of

cloud, our techniques using dynamic chunk size

method. The diagram shows the dynamic chunk size

method.

Fig 2: Cloud Implementation diagram

To upload the data to the cloud , Server load

calculator first calculate the dynamic load of the

server. Then they divide the file size in to different

size of chunks according to the load of the server.

The different size of chunks is uploaded in to theserver. Dynamic chunk size of the file helps to

effectively store the data without the wastage of

storage.

The files are stored using the blob concept.

In blob data are stored in the binary format. To

provide the integrity to the data for each blob storage

values high security factor (HSF) is calculated and

stored with the files. The HSF is calculated by first

setting the middle factor value of the each row, then

xoring the first and last values for each row.

Down loading

The users for attending the class also

register with the cloud provider. The clod providers

give the credentials to the users. These are the second

category of the users in the e-learning, they are

attending the classes. The cloud provider update the

time of the class to the users. The videos are streamed

to the users machine and they can easily watch and

users got the feeling that they are attending the real

classes without any interception .During the time of

streaming they check the HSF value of the blobs. If

the derived value of HSF is same as that of the

previous calculated value then the data stored in the

storage is the correct one and the integrity is

maintained. Otherwise the the data may be corrupted.

Analysis of Result

This section analysis the result of proposed

scheme with the fixed chunk sizes. In the fixed chunk

scheme the file is divided in to fixed size and stored

in the destination while in the dynamic loads the files

are stored on the basis of server loads.


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Fig 3: Upload time for 32Kb

The diagram shows the comparison between

the proposed system and the fixed chunk size. In

fixed chunk size the files are divided in to 32 kb and

uploaded. It takes higher time compared to the

proposed techniques. Whatever may be the file size it

have the increased upload time for the previous

method. The following diagram shows when we

divide the file in to fixed size of 64 kb. Fixed chunk

also have the same performance degradation

compared to the proposed method.

Fig 4: Upload time for 64 Kb

The diagram shows the download time

analysis of fixed and dynamic chunk sizes. The

dynamic chunk size has the less down load time

compared with the fixed one. The figure, shows for

fixed size of 32,64kb. The download time is less than

of 800 ms for various workloads. The down load time

graph is similar to the upload graph.

Fig 5: Download time for 32Kb

On the analysis of above graph clearly we

can found that the Dynamic chunk size having the

less down load time but the one problem is that it has

greater down load time when we upload the 25kb file

in the case of file divided with the 64kb sizes. But for

the 32kb fixed chunk size the proposed scheme has

the lesser down load time.


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Fig 6: Download time for 64 Kb

Scalability

Scalability in cloud computing is the ability

to accommodate the increase number of users. In e-

learning using dynamic chunk size able to provide

the services to the number of users. Proposed e-

learning method accommodate the increased number

of users with the higher security, performance etc.

Fig 7: Scalability

The diagram shows the number of users v/s

performance degradation. For the dynamic chunk size

the performance degradation is less compared with

32kb fixed size.

IV. CONCLUSION

In this paper we havedesign, implemented

and presented the e-learning approach using Dynamic

chunk size. This method having the less download,

upload time .Server load calculation is helpful to

know the dynamic load of the system. Whencompared this approach to other method it provide

the services to the users.

V. REFERENCE

[1]Franc Kozamernik , media streaming over internet an overview

of delivery technologies. ebu technical review october 2002

[2] Di Niu, Hong Xu, Baochun Li and ShuqiaoZhao,Quality-Assured Cloud Bandwidth Auto-Scaling for Video-on-Demand Applications

[3] Chao-Tung Yang and Hsin-ChuanHo,An e-Learning PlatformBased on Grid Architecture, journal of informationscience and engineering 21, 911-928 (2005)

[4]R. Lavanya, and V. Ramachandran,Cloud based videoondemand model with performanceenhancement.,MalaysianJournal of Computer Science, Vol. 24(2),

2011[5] S MohanaSaranya, Dr M Vijayalakshmi, interactive mobilelive video learning system in cloud environment,ieeeinternational

conference on recent tents in informationtechnology, June 3-

5,2011

[6] DimitrisKarakasilis, FotisGeorgatos,TheodorosAlexopoulos,Application of Live Video Streamingover GRID and Cloud infrastructures,2011 11th IEEE

[7]p.Seethalakshmi and V Ramachandran RMI based load sharing

and caching for media on demand international conference on

Digital Aided Modeling and Simulation DAMS 2003 january

[8] Valerie monfortaneMahakhemaja Using SAAs and Cloud

computing For On Demand E-learning service IEEE 10th

international conference on Advanced Learning Technologies 2010

[9] Kong Feng, Yang Xudong,A Study on Grid-based

VODSystem in the E-Learning,2009 International Forum

onInformation Technology and Applications

[10] LavanyaRajendran and RamachandranVeilumuthu,A Cost -Effective Cloud Service for E-Learning Video on Demand,

European Journal of Scientific Research ISSN

1450-216X Vol.55 No.4 (2011), pp.569-579

cloud based e-learning platform using dynamic chunk size

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