Benchmarking Joyent SmartDataCenter for Hadoop MapReduce and MPI Operations

被引:0
|
作者
Luo, Weiliang [1 ]
Golpavar, Nima [1 ]
Cardenas, Carlos [2 ]
Chronopoulos, Anthony T. [1 ]
机构
[1] Univ Texas San Antonio, Dept Comp Sci, 1 UTSA Circle, San Antonio, TX 78249 USA
[2] Joyent Inc, San Francisco, CA 94111 USA
关键词
Cloud computing; Hadoop MapReduce; MPI; benchmarks;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cloud Computing is an ever-growing paradigm shift in computing allowing users commodity access to compute and storage services. As such cloud computing is an emerging promising approach for High Performance Computing (HPC) application development. Automation of resource provision offered by Cloud computing facilitates the eScience programmer usage of computing and storage resources. Currently, there are many commercial services for compute, storage, network and many others from big name companies. However, these services typically do not have performance guarantees associated with them. This results in unexpected performance degradation of user's applications that can be somewhat random to the user. In order to overcome this, a user must be well versed in the tools and technologies that drive Cloud Computing. One of the state of the art cloud systems, Joyent SmartDataCenter, is a cloud system that provides virtual machines (and their processes) the ability to burst CPU capacity automatically and thus is suitable for HPC applications. To help HPC developers, we present a set of Hadoop MapReduce and MPI benchmarks for FlexCloud (a SmartDataCenter installation). Our benchmarks show that this cloud system offers scalable performance for HPC environments.
引用
收藏
页数:6
相关论文
共 50 条
  • [1] MRBS: Towards Dependability Benchmarking for Hadoop MapReduce
    Sangroya, Amit
    Serrano, Damian
    Bouchenak, Sara
    EURO-PAR 2012: PARALLEL PROCESSING WORKSHOPS, 2013, 7640 : 3 - 12
  • [2] On benchmarking collective MPI operations
    Worsch, T
    Reussner, R
    Augustin, W
    RECENT ADVANCES IN PARALLEL VIRTUAL MACHINE AND MESSAGE PASSING INTERFACE, PROCEEDINGS, 2002, 2474 : 271 - 279
  • [3] Join Operations to Enhance Performance in Hadoop MapReduce Environment
    Pagadala, Pavan Kumar
    Vikram, M.
    Eswarawaka, Rajesh
    Reddy, P. Srinivasa
    PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON FRONTIERS IN INTELLIGENT COMPUTING: THEORY AND APPLICATIONS, (FICTA 2016), VOL 2, 2017, 516 : 491 - 500
  • [4] Parallelized Genetic Operations for SBST using Hadoop MapReduce Framework
    Mayandi, Geethapriya
    Arumugam, Chamundeswari
    2014 INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES (ICACCCT), 2014, : 1686 - 1691
  • [5] Micro-benchmarking MPI Neighborhood Collective Operations
    Luebbe, Felix Donatus
    EURO-PAR 2017: PARALLEL PROCESSING, 2017, 10417 : 65 - 78
  • [6] Developing Genetic Algorithms Using Different MapReduce Frameworks: MPI vs. Hadoop
    Salto, Carolina
    Minetti, Gabriela
    Alba, Enrique
    Luque, Gabriel
    ADVANCES IN ARTIFICIAL INTELLIGENCE, CAEPIA 2018, 2018, 11160 : 262 - 272
  • [7] Characterizing and benchmarking stand-alone Hadoop MapReduce on modern HPC clusters
    Shankar, Dipti
    Lu, Xiaoyi
    Wasi-ur-Rahman, Md.
    Islam, Nusrat
    Panda, Dhabaleswar K.
    JOURNAL OF SUPERCOMPUTING, 2016, 72 (12): : 4573 - 4600
  • [8] Characterizing and benchmarking stand-alone Hadoop MapReduce on modern HPC clusters
    Dipti Shankar
    Xiaoyi Lu
    Md. Wasi-ur-Rahman
    Nusrat Islam
    Dhabaleswar K. Panda
    The Journal of Supercomputing, 2016, 72 : 4573 - 4600
  • [9] HPC-Reuse: efficient process creation for running MPI and Hadoop MapReduce on supercomputers
    Thanh-Chung Dao
    Chiba, Shigeru
    2016 16TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), 2016, : 342 - 345
  • [10] Towards Benchmarking the Asynchronous Progress of Non-Blocking MPI Operations
    Medvedev, Alexey V.
    PARALLEL COMPUTING: TECHNOLOGY TRENDS, 2020, 36 : 419 - 428