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	1. Metadata-intensive I/O Optimizations in Parallel File Systems
	Xie Ke,Li Xiuqiao,Wu Qimeng,Xiao Limin,Ruan Li
	Computer Science and Technology 26 April 2013
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	Abstract:With parallel file systems increasingly growing in size, the performance of metadata I/O becomes critical for overall performance. Metadata-intensive applications create a lot of metadata I/O requests with small amount of data, making metadata access become the bottleneck of system. We propose an optimization method based on aggregating and merging requests for metadata-intensive I/O to deal with this problem. Extensive simulations show that the aggregate throughput of intensive file creating can be increased by up to 15.28 times and average response time can be decreased by factors of up to 99.27 percent when the aggregation period and request interval is configured as 0.8ms and 0.025ms respectively. Simulations also show that the aggregate throughput of intensive metadata access can be increased by up to 8.45 times and average response time can be decreased by factors of up to 99.02 percent when the merging period and request interval is configured as 0.4ms and 0.025ms respectively. Meanwhile, experiments show that our method can scale well with the number of metadata servers and clients.
	TO cite this article:Xie Ke,Li Xiuqiao,Wu Qimeng, et al. Metadata-intensive I/O Optimizations in Parallel File Systems[OL].[26 April 2013] http://en.paper.edu.cn/en_releasepaper/content/4539533


	2. Enumerate Strongly Connected Components of Large-scale Graph with MapReduce
	Lu Lv,Lei Xie
	Computer Science and Technology 25 December 2012
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	Abstract:Enumerating strongly connected components in directed graph is the fundamental problem of graph theory. The standard serial algorithm for strongly connected components is based on depth first search, which is difficult to parallelize for large scale graph. In this paper, we propose a nearly linear parallel bi-directional label propagation algorithm to enumerate strongly connected components of large scale graph on MapReduce framework. The algorithm is suitable for large scale graphs and the experiment shows its efficiency and scalability.
	TO cite this article:Lu Lv,Lei Xie. Enumerate Strongly Connected Components of Large-scale Graph with MapReduce[OL].[25 December 2012] http://en.paper.edu.cn/en_releasepaper/content/4502357


	3. Disjoint Paths between Adjacent Vertices in Bijective Connection Networks
	Cheng Baolei ,Jianxi Fan
	Computer Science and Technology 01 December 2011
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	Abstract:Bijective connection networks are a family of hypercube variants, which contains hypercubes, twisted cubes, crossed cubes, Möbius cubes, locally twisted cubes, etc. In this paper, we study the exitence of n disjoint paths between two adjacent vertices in any n-dimensional bijective connection network Xn and develop an parallel Algorithm XST, which can run in O(N) time, to construct n disjoint paths between arbitrary vertex and its 0-neighbor vertex in Xn, where the maximum length of which is less than or equal to n+1.
	TO cite this article:Cheng Baolei ,Jianxi Fan. Disjoint Paths between Adjacent Vertices in Bijective Connection Networks[OL].[ 1 December 2011] http://en.paper.edu.cn/en_releasepaper/content/4453197

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