A PERFORMANCE MODELING LANGUAGE FOR BIG DATA ARCHITECTURES

被引:10
|
作者
Barbierato, Enrico [1 ]
Gribaudo, Marco [2 ]
Iacono, Mauro [3 ]
机构
[1] Univ Turin, Dipartimento Informat, Corso Svizzera 185, I-10129 Turin, Italy
[2] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy
[3] Seconda Univ Napoli, Dipartimento Sci Politiche, I-81100 Caserta, Italy
来源
PROCEEDINGS 27TH EUROPEAN CONFERENCE ON MODELLING AND SIMULATION ECMS 2013 | 2013年
关键词
Big Data; performance modeling; modeling tools; metamodeling; SYSTEMS;
D O I
10.7148/2013-0511
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Big Data applications represent an emerging field, which have proved to be crucial in business intelligence and in massive data management. Big Data promises to be the next big thing in the development of strategical computer applications, even if it requires considerable investment and an accurate resource planning, as the architectures needed to perform at the requisite speed need to scale easily on to a large number of computing nodes. Appropriate management of such architectures benefits from the availability of performance models, to allow developers and administrators to take informed decisions, saving time and experimental work. This paper presents a dedicated modeling language showing firstly how it is possible to ease the modeling process and secondly how the semantic gap between modeling logic and the domain can be reduced.
引用
收藏
页码:511 / +
页数:3
相关论文
共 50 条
  • [1] A Reference Method for Performance Evaluation in Big Data Architectures
    Martins, Wictor Souza
    Kuehne, Bruno Tardiole
    Sobrinho, Rafael Ferreira
    Preti, Fabio
    2020 IEEE 13TH INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2020), 2020, : 280 - 287
  • [2] Performance modeling of big data applications in the cloud centers
    Chao Shen
    Weiqin Tong
    Jenq-Neng Hwang
    Qiang Gao
    The Journal of Supercomputing, 2017, 73 : 2258 - 2283
  • [3] Performance modeling of big data applications in the cloud centers
    Shen, Chao
    Tong, Weiqin
    Hwang, Jenq-Neng
    Gao, Qiang
    JOURNAL OF SUPERCOMPUTING, 2017, 73 (05) : 2258 - 2283
  • [4] Modeling Data Movement Performance on Heterogeneous Architectures
    Bienz, Amanda
    Olson, Luke N.
    Gropp, William D.
    Lockhart, Shelby
    2021 IEEE HIGH PERFORMANCE EXTREME COMPUTING CONFERENCE (HPEC), 2021,
  • [5] An Evaluation of Big Data Architectures
    Garises, Valerie
    Quenum, Jose G.
    PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON DATA SCIENCE, TECHNOLOGY AND APPLICATIONS (DATA), 2019, : 152 - 159
  • [6] Survey of Performance Modeling of Big Data Applications
    Pattanshetti, Tanuja
    Attar, Vahida
    PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE AND ENGINEERING (CONFLUENCE 2017), 2017, : 177 - 181
  • [7] A Comparative Performance of Real-time Big Data Analytic Architectures
    Sanla, Apisit
    Numnonda, Thanisa
    PROCEEDINGS OF 2019 IEEE 9TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2019), 2019, : 674 - 678
  • [8] Big Data Architectures for Vehicle Data Analysis
    Prehofer, Christian
    Mehmood, Shafqat
    2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, : 3404 - 3412
  • [9] Handling Evolution in Big Data Architectures
    Solodovnikova, Darja
    Niedrite, Laila
    BALTIC JOURNAL OF MODERN COMPUTING, 2020, 8 (01): : 21 - 47
  • [10] Modeling IoT and Big Data Impacts to Business Performance
    Jonny
    Kriswanto
    Toshio, Matsumura
    2021 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEE IEEM21), 2021, : 1127 - 1131