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 complaints 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 contains chosen papers of the most convention and papers of the first foreign Workshop on rising subject matters in instant and cellular Computing, ETWMC 2014, the fifth foreign Workshop on clever conversation Networks, IntelNet 2014, and the fifth foreign 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, conversation and regulate applied sciences in clever Transportation approach, 3C in ITS 2014, and the Workshop on safeguard and privateness in desktop and community structures, SPCNS 2014.
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Additional info for Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part II
For WordCount application, the Reduce stage is complex, and takes more time than the Map stage. For Distributed Grep application, the Reduce stage is very simple, and it just collects and sums up the intermediate results. As you can see from these two ﬁgures, as the increase of input text ﬁle size, job completion time also increases. From these two ﬁgures, we can see that there is also only little performance diﬀerence between HybridDFS and HDFS. 4 Scheduler Optimization In this scenario, experiments on WordCount application and Distributed Grep application have also been performed to testify the eﬃciency of the node prioritybased fair scheduling (NPBFS) algorithm.
193–200. IEEE Computer Society (2010) 13. : Improving mapreduce performance in heterogeneous environments. , van Renesse, R. ) OSDI, pp. 29–42. com Abstract. K-means++ is undoubtedly one of the most important initializing algorithms for k-means owing to its provable approximation guarantee to the optimal solution. However, due to its sequential nature, k-means++ requires a large number of iterations to complete the initialization and it becomes ineﬃcient as the size of data increase. Even though scalable k-means++ can drastically reduce the iterations and can be easily applied to the MapReduce systems, but due to its sequential nature, it still requires two MapReduce jobs in each round.
That is, px = px and oj ∗ ∗ d2 (x, Uj−1 ) = φX (Uj−2 ) (1) ∗ d2 (x, Uj−1 ) φX (Uj−1 ) (2) Therefore, the optimal value of oj is oj = φX (Uj−2 ) φX (Uj−1 ) (3) The number of points in Uj−2 contains is smaller than that in Uj−1 , therefore φX (Uj−2 ) ≥ φX (Uj−1 ) and oj ≥ 1. , φX (C) is large. In this section, we propose a method to solve this problem. From the above analysis we know, each center chosen in Mapper phase will be resampled in Reducer phase. Ideally, for the smallest network cost, the best probability pbx of each center in the Mapper phase should be pbx = d2 (x, Uj−1 ) φX (Uj−1 ) (4) For each point x, if pbx > prx , it is chosen in Mapper phase.