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	Parallelizing Video Transcoding Using Map-Reduce-Based Cloud Computing
	Lao, Feng ; Zhang, Xinggong ; Guo, Zongming
	2012
英文摘要	Due to the complexity of video coding, fast transcoding is still a challenge. Various parallel coding methods have been proposed. In this paper, we present a parallel transcoding system over Map/Reduce cloud computing architecture. Input video sequences are divided into segments, and mapped to multiple computers. The sub-tasks are launched in parallel with processing results concatenated to the final output sequences. For heterogeneous clips, computing capacity, and task-launching overhead, the task scheduling over cloud is an NP-hard problem. We propose a low-complexity heuristic algorithm, Max-MCT, to find out the optimal solutions for task scheduling. By estimating the low-bound of finish time, we transform the problem into a virtual knapsack problem. But it is not an optimal solution for the original problem therefore we use a minimal complete time (MCT) algorithm to minimize the entire finish time. We carry out extensive experiments on numerical simulations. The results verified that our algorithm outperforms the existing algorithms.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000316903703026&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Engineering, Electrical & Electronic; EI; CPCI-S(ISTP); 0
语种	英语
DOI标识	10.1109/ISCAS.2012.6271923
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/321189]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Lao, Feng,Zhang, Xinggong,Guo, Zongming. Parallelizing Video Transcoding Using Map-Reduce-Based Cloud Computing. 2012-01-01.

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