Fluid Petri Nets for the Performance Evaluation of MapReduce Applications

Eugenio Gianniti; Alessandro Rizzi; Enrico Barbierato; Marco Gribaudo; Danilo Ardagna

10th EAI International Conference on Performance Evaluation Methodologies and Tools

Research Article

Fluid Petri Nets for the Performance Evaluation of MapReduce Applications

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/eai.25-10-2016.2267025,
    author={Eugenio Gianniti and Alessandro Rizzi and Enrico Barbierato and Marco Gribaudo and Danilo Ardagna},
    title={Fluid Petri Nets for the Performance Evaluation of MapReduce Applications},
    proceedings={10th EAI International Conference on Performance Evaluation Methodologies and Tools},
    publisher={ACM},
    proceedings_a={VALUETOOLS},
    year={2017},
    month={5},
    keywords={map reduce hadoop fluid petri nets},
    doi={10.4108/eai.25-10-2016.2267025}
}

Eugenio Gianniti
Alessandro Rizzi
Enrico Barbierato
Marco Gribaudo
Danilo Ardagna
Year: 2017
Fluid Petri Nets for the Performance Evaluation of MapReduce Applications
VALUETOOLS
ACM
DOI: 10.4108/eai.25-10-2016.2267025

Eugenio Gianniti¹^,*, Alessandro Rizzi¹, Enrico Barbierato¹, Marco Gribaudo¹, Danilo Ardagna¹

1: Politecnico di Milano

*Contact email: eugenio.gianniti@polimi.it

Abstract

Big Data applications allow to successfully analyze large amount 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 new modeling approach based on fluid Petri nets to envision MapReduce jobs execution time.

Keywords: map reduce hadoop fluid petri nets

Published: 2017-05-03
Publisher: ACM

: http://dx.doi.org/10.4108/eai.25-10-2016.2267025