Fluid Petri nets for the performance evaluation of MapReduce applications

E. Gianniti; A. M. Rizzi; Enrico Barbierato; M. Gribaudo; D. Ardagna

doi:10.4108/eai.25-10-2016.2267025

Fluid Petri nets for the performance evaluation of MapReduce applications

E. Gianniti
, A. M. Rizzi
, Enrico Barbierato
, M. Gribaudo
, D. Ardagna

Polytechnic University of Milan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Big Data applications allow to successfully analyze large amounts of data not necessarily structured, though at the same time they present new challenges. For example, predicting the performance of frameworks such as Hadoop can be a costly task, hence the necessity to provide models that can be a valuable support for designers and developers. This paper provides a new contribution in studying a novel modeling approach based on fluid Petri nets to predict MapReduce jobs execution time. The experiments we performed at CINECA, the Italian supercomputing center, have shown that the achieved accuracy is within 16% of the actual measurements on average.

Original language	English
Title of host publication	ValueTools 2016 - 10th EAI International Conference on Performance Evaluation Methodologies and Tools
Publisher	Association for Computing Machinery
Pages	243-250
Number of pages	8
ISBN (Print)	978-163190141-6
DOIs	https://doi.org/10.4108/eai.25-10-2016.2267025
Publication status	Published - 2017

All Science Journal Classification (ASJC) codes

Instrumentation

Keywords

Fluid Petri nets
Hadoop
Map Reduce

Access to Document

10.4108/eai.25-10-2016.2267025

Cite this

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title = "Fluid Petri nets for the performance evaluation of MapReduce applications",

abstract = "Big Data applications allow to successfully analyze large amounts of data not necessarily structured, though at the same time they present new challenges. For example, predicting the performance of frameworks such as Hadoop can be a costly task, hence the necessity to provide models that can be a valuable support for designers and developers. This paper provides a new contribution in studying a novel modeling approach based on fluid Petri nets to predict MapReduce jobs execution time. The experiments we performed at CINECA, the Italian supercomputing center, have shown that the achieved accuracy is within 16\% of the actual measurements on average.",

keywords = "Fluid Petri nets, Hadoop, Map Reduce, Fluid Petri nets, Hadoop, Map Reduce",

author = "E. Gianniti and Rizzi, \{A. M.\} and Enrico Barbierato and M. Gribaudo and D. Ardagna",

year = "2017",

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Fluid Petri nets for the performance evaluation of MapReduce applications. / Gianniti, E.; Rizzi, A. M.; Barbierato, Enrico et al.
ValueTools 2016 - 10th EAI International Conference on Performance Evaluation Methodologies and Tools. Association for Computing Machinery, 2017. p. 243-250.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Fluid Petri nets for the performance evaluation of MapReduce applications

AU - Gianniti, E.

AU - Rizzi, A. M.

AU - Barbierato, Enrico

AU - Gribaudo, M.

AU - Ardagna, D.

PY - 2017

Y1 - 2017

N2 - Big Data applications allow to successfully analyze large amounts of data not necessarily structured, though at the same time they present new challenges. For example, predicting the performance of frameworks such as Hadoop can be a costly task, hence the necessity to provide models that can be a valuable support for designers and developers. This paper provides a new contribution in studying a novel modeling approach based on fluid Petri nets to predict MapReduce jobs execution time. The experiments we performed at CINECA, the Italian supercomputing center, have shown that the achieved accuracy is within 16% of the actual measurements on average.

AB - Big Data applications allow to successfully analyze large amounts of data not necessarily structured, though at the same time they present new challenges. For example, predicting the performance of frameworks such as Hadoop can be a costly task, hence the necessity to provide models that can be a valuable support for designers and developers. This paper provides a new contribution in studying a novel modeling approach based on fluid Petri nets to predict MapReduce jobs execution time. The experiments we performed at CINECA, the Italian supercomputing center, have shown that the achieved accuracy is within 16% of the actual measurements on average.

KW - Fluid Petri nets

KW - Hadoop

KW - Map Reduce

KW - Fluid Petri nets

KW - Hadoop

KW - Map Reduce

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DO - 10.4108/eai.25-10-2016.2267025

M3 - Conference contribution

SN - 978-163190141-6

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BT - ValueTools 2016 - 10th EAI International Conference on Performance Evaluation Methodologies and Tools

PB - Association for Computing Machinery

ER -

Fluid Petri nets for the performance evaluation of MapReduce applications

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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