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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/93541

    Title: Dynamically Iterative MapReduce
    Authors: Lee, Wei-Tsong;Wu, Ming-Zhi;Wei, Hsin-Wen;Yu, Fang-Yi;Lin, Yu-Sun
    Contributors: 淡江大學電機工程學系
    Keywords: Dynamically iterative MapReduce;K-Means;Particle swarm optimization (PSO);Genetic algorithm (GA);Simulated annealing (SA)
    Date: 2013-11-01
    Issue Date: 2014-01-22 16:43:39 (UTC+8)
    Publisher: 臺北市:臺灣學術網路管理委員會
    Abstract: MapReduce is a distributed and parallel computing model for data-intensive tasks with features of optimized scheduling, flexibility, high availability, and high manageability. MapReduce can work on various platforms; however, MapReduce is not suitable for iterative programs because the performance may be lowered by frequent disk I/O operations. In order to improve system performance and resource utilization, we propose a novel MapReduce framework named Dynamically Iterative MapReduce (DIMR) to reduce numbers of disk I/O operations and the consumption of network bandwidth by means of using dynamic task allocation and memory management mechanism. We show that DIMR is promising with detail discussions in this paper.
    Relation: 網際網路技術學刊=Journal of Internet Technology 14(6),頁 953-962
    DOI: 10.6138/JIT.2013.14.6.10
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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