Titlebook: Algorithms and Architectures for Parallel Processing; 14th International C Xian-he Sun,Wenyu Qu,Lei Liu Conference proceedings 2014 Springe

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/153077.jpg

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https://doi.org/10.1007/978-3-540-78457-9er nodes and heterogeneous desktop PCs in Internet or Intranet can be integrated to form a hybrid computing environment. In this way, the computation and storage capability of large-scale desktop PCs can be fully utilized to process large-scale datasets. HybridMR relies on a hybrid distributed file

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https://doi.org/10.1007/978-3-662-59724-8lution. However, due to its sequential nature, .-means++ requires a large number of iterations to complete the initialization and it becomes inefficient as the size of data increase. Even though scalable .-means++ can drastically reduce the iterations and can be easily applied to the MapReduce syste

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https://doi.org/10.1007/978-3-540-71131-5ork profiling. With the development of internet, network attacks occur frequently such as worm spreading, DDoS attack and port scanning and so on. One common characteristic of these attacks is that they usually generate a lot of traffic connections in a short time which will lead the host cardinalit

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https://doi.org/10.1007/978-3-540-71131-5 access delay. Data locality is becoming one of the most critical factors to affect performance of MapReduce clusters. As machines in MapReduce clusters have large memory capacities, which are often underutilized, in-memory prefetching input data is an effective way to improve data locality. However