7th International Conference on Performance Evaluation Methodologies and Tools

Research Article

Modeling Apache Hive based applications in Big Data architectures

  • @INPROCEEDINGS{10.4108/icst.valuetools.2013.254398,
        author={Marco Gribaudo and Enrico Barbierato and Mauro Iacono},
        title={Modeling Apache Hive based applications in Big Data architectures},
        proceedings={7th International Conference on Performance Evaluation Methodologies and Tools},
        publisher={ICST},
        proceedings_a={VALUETOOLS},
        year={2014},
        month={1},
        keywords={big data models hadoop multiformalism models},
        doi={10.4108/icst.valuetools.2013.254398}
    }
    
  • Marco Gribaudo
    Enrico Barbierato
    Mauro Iacono
    Year: 2014
    Modeling Apache Hive based applications in Big Data architectures
    VALUETOOLS
    ACM
    DOI: 10.4108/icst.valuetools.2013.254398
Marco Gribaudo1,*, Enrico Barbierato2, Mauro Iacono3
  • 1: Politecnico di Milano
  • 2: Universita' di Torino
  • 3: Seconda Università degli Studi di Napoli
*Contact email: gribaudo@elet.polimi.it

Abstract

Performance prediction for Big Data applications is a power- ful tool supporting designers and administrators in achieving a better exploitation of their computing resources. Big Data architectures are complex, continuously evolving and adap- tive, thus a rapid design and verification modeling approach can be fit to the needs. As a result, a minimal semantic gap between models and applications would enable a wider number of designers to directly benefit from the results. The paper presents a multiformalism modeling approach based on a one-to-one mapping of Apache Hive querying primitives to modeling primitives. This approach exploits a combina- tion of proper Big Data specific submodels and Petri nets to enable modeling of conventional application logic.