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Algorithms and Architectures for Parallel Processing: 14th by Xian-he Sun, Wenyu Qu, Ivan Stojmenovic, Wanlei Zhou,

By Xian-he Sun, Wenyu Qu, Ivan Stojmenovic, Wanlei Zhou, Zhiyang Li, Hua Guo, Geyong Min, Tingting Yang, Yulei Wu, Lei Liu (eds.)

This quantity set LNCS 8630 and 8631 constitutes the court cases of the 14th foreign convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2014, held in Dalian, China, in August 2014. The 70 revised papers offered within the volumes have been chosen from 285 submissions. the 1st quantity includes chosen papers of the most convention and papers of the first foreign Workshop on rising themes in instant and cellular Computing, ETWMC 2014, the fifth overseas Workshop on clever communique Networks, IntelNet 2014, and the fifth overseas Workshop on instant Networks and Multimedia, WNM 2014. the second one quantity includes chosen papers of the most convention and papers of the Workshop on Computing, communique and keep an eye on applied sciences in clever Transportation approach, 3C in ITS 2014, and the Workshop on defense and privateness in desktop and community platforms, SPCNS 2014.

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Extra resources for Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part II

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Xn }, a set of data points. , ck }. 1 C ← sample a point uniformly at random from X 2 ψ ← φX (C) 3 for O(logψ) times do 4 C sample each point x ∈ X independently with probability px = 5 6 7 3 d2 (x,C) φX (C) C ← C ∪ C , compute φX (C) For x ∈ C, set wx to be the number of points in X closer to x than any other point in C Recluster the weighted points in C into k clusters Our Method In this section, we first introduce the Parallel Scalable k-means++ with MapReduce (PSKM++), and then present our improved version of PSKM++, Oversampling and Refining (OnR).

Fig. 5(b) shows the results of average throughput of concurrent read clients. When the number of concurrent clients is more than 64, less than 3 chunks are allocated to each client in average. As the increase of concurrent read clients, the metadata query load and data traffic increases, which causes a decrease of average throughput. The same as Scenario 1, HDFS also outperforms HybridDFS when 24 cluster nodes and 72 desktop PCs are used, but there is only little difference between average throughput of HybridDFS and HDFS.

In Reducer phase, one Reducer adds all local clustering cost, and uses it to remove the oversampled points generated from o. The major contributions of this paper are: 1. We propose Oversampling and Refing method which is an efficient scalable k-means++ algorithm with MapReduce. It uses only one MapReduce job to complete the task of choosing centers and computing clustering cost in each round, avoiding too many jobs on multiple machines and thus reducing a large number of I/O cost. 2. To reduce the network cost and the running time caused by too large oversampling factor o, our method OnR estimates the global cost by the local clustering cost and uses it to remove some oversampling points in Mapper phase.

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