Cloud Based E-Learning Platform Using Dynamic Chunk Size

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1 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 the fast 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 using which 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 ISSN: Page 406

2 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 cloudbased 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 using reusable 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 with the 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 the video 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 anywhere in 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. ISSN: Page 407

3 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 the server. Dynamic chunk size of the file helps to effectively store the data without the wastage of storage. 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. 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 value s 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 user s 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. Fig 2: Cloud Implementation diagram ISSN: Page 408

4 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 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 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. Fig 4: Upload time for 64 Kb ISSN: Page 409

5 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. When compared this approach to other method it provide the services to the users. V. REFERENCE Scalability Fig 6: Download time for 64 Kb 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. [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 Shuqiao Zhao, Quality-Assured Cloud Bandwidth Auto-Scaling for Videoon-Demand Applications [3] Chao-Tung Yang and Hsin-ChuanHo, An e-learning Platform Based on Grid Architecture, journal of information science and engineering 21, (2005) [4]R. Lavanya, and V. Ramachandran, Cloud based video ondemand model with performance enhancement.,malaysianjournal of Computer Science, Vol. 24(2), 2011 [5] S MohanaSaranya, Dr M Vijayalakshmi, interactive mobile live 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 Streaming over GRID and Cloud infrastructures, th 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 10 th 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 X Vol.55 No.4 (2011), pp 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. ISSN: Page 410

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