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Massive Video Conversion Platform Design and Implementation Based on Cloud Computing

Author: Liu
Tutor: LiYingZhuang
School: Dalian University of Technology
Course: Applied Computer Technology
Keywords: Massive Video Converter Cloud Computing Architecture Task scheduling strategy Eucalyptus Hadoop
CLC: TP311.52
Type: Master's thesis
Year: 2011
Downloads: 164
Quote: 0
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Abstract


With the rapid development of the Internet , the increasingly high demand for the video conversion business , With the accelerating process of triple play , video conversion on top of the computer network , telecommunication network and cable television business data showing sea quantify coexistence of multi-platform , the diverse coding standards salient features . If , however , the traditional video transcoding , both in terms of efficiency or versatility are lacking. Cloud computing technology as a produce business computing model in scientific computing model in many ways to meet the demand for video conversion business new solutions . This paper first introduces the basic philosophy and the history of the development of cloud computing , and then analyze the cloud computing model several applications . Two cloud computing architecture level , that layer of Eucalyptus of Iaas and Paas layer , Hadoop , combined with the characteristics of the the massive video conversion business , considering the characteristics of the cloud computing architecture , this paper designed a the massive video converter cloud platform . In this paper , for the massive video conversion Cloud platform architecture and functionality of the detailed design and implementation , including : the cloud control layer , the cluster control layer node control layer , as well as the virtual network . Characteristics of the massive video conversion business , cloud platform designed business processes . We know that if you want to implement cloud computing resource scheduling capabilities of the architecture on two levels , ie virtual machine scheduling and video task scheduling capabilities , and to ensure that the cloud platform to efficiently , reliably , stable operation , it must achieve three important mechanisms: resource the dynamic handling mechanism , task processing , fault-tolerant mechanism , platform error handling mechanism to this end , this paper designs and implements cloud platform four core modules : service scheduling module , task processing module , dynamic processing module , message processing module. The four modules coordination and cooperation in order to ensure the normal operation of the cloud platform . Finally, the in laboratory network built environment , to achieve cloud platform to test and verify the validity and correctness of the cloud platform .

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