Performance analysis of MapReduce program in heterogeneous cloud computing

被引:8
作者
Lin, Wenhui [1 ,2 ]
Liu, Jun [1 ]
机构
[1] Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications
[2] Technology Research Institute, Aisino Corporation
关键词
Benchmark; Cloud computing; Hadoop; Performance analysis;
D O I
10.4304/jnw.8.8.1734-1741
中图分类号
学科分类号
摘要
The research of Hadoop is an important part of cloud computing industry, and Hadoop performance research is a key research direction. The Hadoop performance analysis as a basic work can provide important reference for other performance optimization researches. In this paper, based on previous researches of server performance analysis, we propose a node performance measurement method on Hadoop. We describe in detail how to measure the performance value of each node in heterogeneous Hadoop cluster and evaluate measurement results by running MapReduce programs. Meanwhile, the method has also been implemented and evaluated in real-world Hadoop cluster. Experiment results show that the method can accurately measure the performance value of each node. Based on this research, users can have a comprehensive and objective understanding of their own Hadoop cluster and then make optimization and improvement on Hadoop. © 2013 ACADEMY PUBLISHER.
引用
收藏
页码:1734 / 1741
页数:7
相关论文
共 50 条
  • [41] Analysis of Network Infrastructure Performance on Cloud Computing
    Gustamas, R. Gargista
    Shidik, Guruh Fajar
    2017 INTERNATIONAL SEMINAR ON APPLICATION FOR TECHNOLOGY OF INFORMATION AND COMMUNICATION (ISEMANTIC), 2017, : 169 - 174
  • [42] Analysis of Performance Variability in Public Cloud Computing
    Ericson, Jamie
    Mohammadian, Masoud
    Santana, Fabiana
    2017 IEEE 18TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IEEE IRI 2017), 2017, : 308 - 314
  • [43] MapReduce for Scientific Computing
    Jakovits, Pelle
    Srirama, Satish Narayan
    Vainikko, Eero
    APPLICATIONS, TOOLS AND TECHNIQUES ON THE ROAD TO EXASCALE COMPUTING, 2012, 22 : 117 - 124
  • [44] Analysis of MapReduce Scheduling and Its Improvements in Cloud Environment
    D'Souza, Sofia
    Chandrasekaran, K.
    2015 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, INFORMATICS, COMMUNICATION AND ENERGY SYSTEMS (SPICES), 2015,
  • [45] Result Verification Mechanism for MapReduce Computation Integrity in Cloud Computing
    Bendahmane, Ahmed
    Essaaidi, Mohammad
    el Moussaoui, Ahmed
    Younes, Ali
    PROCEEDINGS OF 2012 INTERNATIONAL CONFERENCE ON COMPLEX SYSTEMS (ICCS12), 2012, : 469 - 474
  • [46] A Content-wise Data Placement Policy for Improving the Performance of MapReduce-based Video Processing Applications in Cloud Computing
    SaatiAlsoruji, Eihab
    2020 IEEE 13TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD 2020), 2020, : 166 - 175
  • [47] A result correctness verification mechanism for cloud computing based on MapReduce
    Liu, Ziao
    Jiang, Tao
    Tao, Xiaoling
    INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS, 2019, 11 (04) : 526 - 539
  • [48] Simulation of Free Surface Flows Based on MapReduce in Cloud Computing
    Liu Xu
    Ban Xiaojuan
    Wang Mohan
    CHINA COMMUNICATIONS, 2011, 8 (06) : 28 - 35
  • [49] A SERVICE INTEGRITY ASSURANCE FRAMEWORK FOR CLOUD COMPUTING BASED ON MAPREDUCE
    Ren, Yulong
    Tang, Wen
    2012 IEEE 2ND INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND INTELLIGENT SYSTEMS (CCIS) VOLS 1-3, 2012, : 240 - 244
  • [50] MAPREDUCE-APRIORI ALGORITHM UNDER CLOUD COMPUTING ENVIRONMENT
    Chang, Xue-Zhou
    PROCEEDINGS OF 2015 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOL. 2, 2015, : 637 - 641