English  |  正體中文  |  简体中文  |  Items with full text/Total items : 58286/91808 (63%)
Visitors : 13812936      Online Users : 77
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/92999

    Title: LaSA: A Locality-aware Scheduling Algorithm for Hadoop-MapReduce Resource Assignment
    Authors: Chen, Tseng-Yi;Wei, Hsin-Wen;Wei, Ming-Feng;Chen, Ying-Jie;Hsu, Tsan-sheng;Shih, Wei-Kuan
    Contributors: 淡江大學資訊管理學系
    Date: 2013-05
    Issue Date: 2013-11-04 15:38:25 (UTC+8)
    Abstract: Cloud computing has become more popular for a decade; it has been under continuous development with advances in architecture, software, and network. Hadoop-MapReduce is a common software framework processing parallelizable problem across big datasets using a distributed cluster of processors or stand-alone computers. Cloud Hadoop-MapReduce can scale incrementally in the number of processing nodes. Hence, the Hadoop-MapReduce is designed to provide a processing platform with powerful computation. Network traffic is always a most important bottleneck in data-intensive computing and network latency decreases significant performance in data parallel systems. Network bottleneck is caused by network bandwidth and the network speed is much slower than disk data access. So that, good data locality can reduces network traffic and increases performance in data-intensive HPC systems. However, Hadoop's scheduler has a defect of data locality in resource assignment. In this paper, we present a locality-aware scheduling algorithm (LaSA) for Hadoop-MapReduce scheduler. Firstly, we propose a mathematical model of weight of data interference in Hadoop scheduler. Secondly, we present the LaSA algorithm to use weight of data interference to provide data locality-aware resource assignment in Hadoop scheduler. Finally, we build an experimental environment with 3 cluster and 35 VMs to verify the LaSA's performance.
    Relation: 2013 International Conference on Collaboration Technologies and Systems (CTS), pp.342-346
    Appears in Collections:[Graduate Institute & Department of Information Management] Proceeding

    Files in This Item:

    File Description SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback