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 lawsuits 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 awarded within the volumes have been chosen from 285 submissions. the 1st quantity includes chosen papers of the most convention and papers of the first overseas Workshop on rising issues in instant and cellular Computing, ETWMC 2014, the fifth overseas Workshop on clever conversation 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, verbal exchange and keep an eye on applied sciences in clever Transportation procedure, 3C in ITS 2014, and the Workshop on safeguard and privateness in machine 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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Oversampling and Refing with MapReduce Fast Scalable k-means++ Algorithm with MapReduce 21 k-means++ in each iteration. Thus, our method requires a refining method to remove the oversampled centers in Reducer phase. Algorithm 2. Oversampling and Refining Input: k, X, , o, c1 Output: initialing centers /* job 1: Computing ψ */ 1 m Mappers read X in parallel and read U0 = c1 . Each of them computes φXi (U0 ). 2 All costs φXi (U0 ) are shuffled to one Reducer. 3 One Reducer sums all φXi (U0 ). 4 Outputs ψ = φX (U0 ).

Thus, we only record the I/O cost and network cost in each round. The experimental results are summarized in Figure 5. From Fig. 5(a) we can see, for each round, the network cost of both OnR and PSKM++ are small and they are in [140KB, 200KB]. However, except for the first round, the network cost of OnR is larger than that of PSKM++, due to the reason that OnR chooses more centers (the expected number of chosen points is o ∗ ) in Mapper phase than PSKM++ (the expected number of chosen points is ).

We also evaluated job completion time, while we compared two scheduling policies: 1) using NPBFS scheduler; 2) not using NPBFS scheduler. We measure how many performance improvements are caused by NPBFS scheduler. If the NPBFS scheduler is not used, the server 12 B. Tang, H. He, and G. Fedak WordCount Distributed Grep 350 HybridMR − Cluster & PC Hadoop − Cluster & PC Hadoop − Cluster only 700 HybridMR − Cluster & PC Hadoop − Cluster & PC Hadoop − Cluster only 300 Job completion time (s) Job completion time (s) 600 500 400 300 200 250 200 150 100 50 100 0 0 2 4 6 8 10 0 0 12 2 4 Text size (GB) 6 8 10 12 Text size (GB) (a) WordCount application (b) Distributed Grep application Fig.

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