A PERFORMANCE MODELING LANGUAGE FOR BIG DATA ARCHITECTURES

被引：12

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

页码：511 / +

页数：3

共 50 条

[11] Modeling IoT and Big Data Impacts to Business Performance [J].

Jonny ;

Kriswanto ;

Toshio, Matsumura .

2021 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEE IEEM21), 2021, :1127-1131

[12] Adaptive incremental transfer learning for efficient performance modeling of big data workloads [J].

Garralda-Barrio, Mariano ;

Eiras-Franco, Carlos ;

Bolon-Canedo, Veronica .

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2025, 166

[13] Enterprise Architectures for Supporting the Adoption of Big Data [J].

Gong, Yiwei ;

Janssen, Marijn .

DG.O 2017: THE PROCEEDINGS OF THE 18TH ANNUAL INTERNATIONAL CONFERENCE ON DIGITAL GOVERNMENT RESEARCH: INNOVATIONS AND TRANSFORMATIONS IN GOVERNMENT, 2017, :505-510

[14] Big Data Software Architectures: An Updated Review [J].

Remigio da Costa, Tiago Vinicius ;

Cavalcante, Everton ;

Batista, Thais .

COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2022, PT I, 2022, 13375 :477-493

[15] Modeling and Simulation in Performance Optimization of Big Data Processing Frameworks [J].

Ranjan, Rajiv .

IEEE CLOUD COMPUTING, 2014, 1 (04) :14-19

[16] Performance Modeling of System Architectures [J].

Fred Rose ;

John Shackleton ;

Carl Hein .

Journal of VLSI signal processing systems for signal, image and video technology, 1997, 15 :97-109

[17] Modeling and Evaluating the Effects of Big Data Storage Resource Allocation in Global Scale Cloud Architectures [J].

Barbierato, Enrico ;

Gribaudo, Marco ;

Iacono, Mauro .

INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING, 2016, 12 (02) :1-20

[18] Big Data for Infectious Disease Surveillance and Modeling [J].

Bansal, Shweta ;

Chowell, Gerardo ;

Simonsen, Lone ;

Vespignani, Alessandro ;

Viboud, Cecile .

JOURNAL OF INFECTIOUS DISEASES, 2016, 214 :S375-S379

[19] Performance Analysis of Big Data ETL Process over CPU-GPU Heterogeneous Architectures [J].

Lee, Suyeon ;

Park, Sungyong .

2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOPS (ICDEW 2021), 2021, :42-47

[20] Performance Modeling and Analysis of a Hadoop Cluster for Efficient Big Data Processing [J].

Lim, JongBeom ;

Ahnh, Jong-Suk ;

Lee, Kang-Woo .

ADVANCED SCIENCE LETTERS, 2016, 22 (09) :2314-2319

← 1 2 3 4 5 →