Nowadays video is being produced and consumed in more component representation formats, more device types and over a variety of networks than ever. Transmission of video through network takes more time. So Video Transcoding is a very important factor when the video is moved between various heterogeneous clients in the cloud environment. Transcoding is a process of translating one coded form of video into another. However most of the time transcoding becomes computationally intensive and time consuming process. This proposed cloud system has four of the video compression standards such as Low Quality Encoding , Standard Quality Encoding , High Quality Encoding and High Definition. These standards achieve better compression performance with Quality. In general compression process takes more time. Map Reduce is used for managing a work in a considerable short period of time. This in turn helps in faster and efficient video transcoding. These Four standards are embedded into the Hadoop Distributed File System implementation and trial runs were done. Using the HDFS Map Reduce functionality, the video is splited using 64 MB blocks (Segments of Streams) and processed separately for maintaining efficiency in a time based aspect. This system helps in reducing the size of the video slice thereby providing opportunity for efficient transmission in quicker time. Quality and compression time has produced efficient results.
Keywords: Cloud Computing, Video Encoding, Hadoop, Map Reduce, FFMPEG
Introduction
Video content is being produced, transported and consumed in more ways and devices than ever. Meanwhile a seamless interaction between video content producing, transporting and consuming devices is required. The difference in device, networ...
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...limit to which the extent of the elasticity property can be utilized. This can be solved by optimization of the chunks generated to be limited to a extent that restricts based on the Nodes available in the cloud source. The performance of the clusters in a Hadoop Map reduce does not depend on the hardware it works upon. The performance of the Hadoop map reduce shall be increased by fine-tuning certain aspects, that will indirectly increase the performance ratio. Some of the parameters that can be fine-tuned are cluster specifications and processing complexity. Using the HDFS Map Reduce functionality, the video is splited using 64 MB blocks (Segments of Streams) and processed separately for maintaining efficiency in a time based aspect. This system helps in reducing the size of the video slice thereby providing opportunity for efficient transmission in quicker time.
Kerberos provides a secure authentication scheme. Authentication is needed to restrict the intruders and malicious users. The major security issues discussed are privacy of the data, integrity of data and authentication mechanism which is not there in Hadoop. Hadoop supports Kerberos for authentication and many security features can be configured with the Hadoop to restrict the accessibility of the data. The data can be associated with the user names or group names in which data can be accessed. Kerberos is a conventional authentication system, improved authentication systems can be used which are more secure and efficient than
Cloud is the result of a decade research in the field of distributing computing , utility computing, virtualization , grid computing and more recently software, network services and web technology which is changeable evolution on demanding technology and services also as looking to the rapid growth of cloud computing which have changed the global computing infrastructure as well as the concept of computing resources toward cloud infrastructure. The important and interest of cloud computing increasing day by day and this technology receives more and more attention in the world (Jain, 2014) the mostly widely used definition of cloud computing is introduced by NIST “as a model for enabling a convenient on demand network access
A TV signal is captured by a camera and then manipulated during program production. At this point the video must be at its highest quality and full bandwidth for recording, editing and special effects purposes. Then the TV signal needs to be compressed for economical transmission and storage. The possible efficiency of compression depends on a couple of factors. If a signal will be further edited and manipulated in the receiving studio it must maintain a relatively high quality and therefore can’t be compressed as much as a signal that will be sent directly to the viewer’s TV set. Also, the extent to which a signal can be successfully compressed depends on the type of program (E.g. movies can be compressed more than sports). Nevertheless, a typical program mix will fit up to 10 digital television channels on one transmission line.
DFS promises that its system can be extended by adding more nodes to accommodate data’s growing. Also it can remove those not frequently used data from overloaded nodes to those light nodes to reduce network traffic. Scalability is the capability of a system, network, or process to handle a growing amount of work, or its potential to be enlarged in order to accommodate that growth.
Big Data is characterized by four key components, volume, velocity, variety, and value. Furthermore, Big Data can come from an array sources such as Facebook, Twitter, call
To demonstrate the competency skills expressed in this cover sheet, I have provided three evidence items, the research paper on interpreting in the video relay setting, the research paper on interpreting systems, and the community interpreter resource guide. Together this evidence demonstrates of my ability to discuss state and national interpreter certification as well as the scope and authority of state and federal laws which impact D/deaf people and interpreters.
Sudipto, D. Nishumura, S. Agrawal, D. A, E, Abbadi (2010). Department of Computer Sciene. Live Database Migration for Elasticity migration in multitenant database for cloud platforms. Available at: http://cs.ucsb.edu/research/tech_reports/reports/2010-09.pdf (accessed November 30, 2013)
Google File System (GFS) was developed at Google to meet the high data processing needs. Hadoop’s Distributed File System (HDFS) was originally developed by Yahoo.Inc but it is maintained as an open source by Apache Software Foundation. HDFS was built based on Google’s GFS and Map Reduce. As the internet data was rapidly increasing there was a need to store the large data coming so Google developed a distributed file system called GFS and HDFS was developed to meet the different client needs. These are built on commodity hardware so the systems often fail. To make the systems reliable the data is replicated among multiple nodes. By default minimum number of replicas is 3. Millions of files and large files are common with these types of file systems. Data is more often read than writing. Large streaming needs and small random needs are supported.
When they wanted to save photos online instead of on your personal computer, they are able to use “cloud computing” service. Cloud computing means that the transfer of computing data or information over the internet. Not just to keep data in your personal computer, they are able to save the data on internet server to open their data in any computer. In this report we will walk through about what is cloud computing, what kinds of model did cloud computing have, types of cloud computing, benefits of cloud computing, and security.
The cloud storage services are important as it provides a lot of benefits to the healthcare industry. The healthcare data is often doubling each and every year and consequently this means that the industry has to invest in hardware equipment tweak databases as well as servers that are required to store large amounts of data (Blobel, 19). It is imperative to understand that with a properly implemented a cloud storage system, and hospitals can be able to establish a network that can process tasks quickly with...
For viewers new to streaming video deciding which device is the best choice for them will be determined by price, features and the availability of their desired
Evolution of cloud computing over the past few years is potentially one of the major advances in the history of computing. Many computing applications are general-purpose in nature, and therefore offer tremendous economies of scale if their supply can be consolidated (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011).
These are common communication items at most people’s households. There are many communication features as well as software on a laptop and computer which are helpful in a day to day lifestyle.
On the other hand, if you'd like to use your cloud storage for files that are hundreds of megabytes or even gigabytes in size, it's quite troublesome to download that huge file and share it to others. They may have very slow connection, which makes it quite impractical to download such huge files. You'll be better off burning those files onto a DVD or copy them to a portable hard drive or even USB flash drives. From here on, we'll just have to wait and see. Who knows? As internet connections become faster and faster and as more companies offer cloud storage service, we may see huge improvements in the future.