Quantitative Analysis of Apache Storm Applications: The NewsAsset Case Study

José I. Requeno (), José Merseguer (), Simona Bernardi (), Diego Perez-Palacin (), Giorgos Giotis () and Vasilis Papanikolaou ()
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José I. Requeno: Universidad de Zaragoza
José Merseguer: Universidad de Zaragoza
Simona Bernardi: Universidad de Zaragoza
Diego Perez-Palacin: Universidad de Zaragoza
Giorgos Giotis: Athens Technology Center
Vasilis Papanikolaou: Athens Technology Center

Information Systems Frontiers, 2019, vol. 21, issue 1, No 5, 67-85

Abstract: Abstract The development of Information Systems today faces the era of Big Data. Large volumes of information need to be processed in real-time, for example, for Facebook or Twitter analysis. This paper addresses the redesign of NewsAsset, a commercial product that helps journalists by providing services, which analyzes millions of media items from the social network in real-time. Technologies like Apache Storm can help enormously in this context. We have quantitatively analyzed the new design of NewsAsset to assess whether the introduction of Apache Storm can meet the demanding performance requirements of this media product. Our assessment approach, guided by the Unified Modeling Language (UML), takes advantage, for performance analysis, of the software designs already used for development. In addition, we converted UML into a domain-specific modeling language (DSML) for Apache Storm, thus creating a profile for Storm. Later, we transformed said DSML into an appropriate language for performance evaluation, specifically, stochastic Petri nets. The assessment ended with a successful software design that certainly met the scalability requirements of NewsAsset.

Keywords: Apache storm; UML; Petri nets; Software performance; Software reuse (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10796-018-9851-x

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