Modeling and Analysis of Hadoop MapReduce Systems for Big Data Using Petri Nets

Information technological advances have significantly increased large volumes of corporate datasets, which have also created a wide range of business opportunities related to big data and cloud computing. Hadoop is a popular programming framework used for the setup of a cloud computing system. The MapReduce framework forms a core of the Hadoop program for parallel computing and its parallel framework can greatly increase the efficiency of big data analysis. This paper aims to adopt a Petri net (PN) to create a visual model of the MapReduce framework and to analyze its reachability property. We present a real big data analysis system to demonstrate the feasibility of the PN model, to describe the internal procedure of the MapReduce framework in detail, to list common errors and to propose an error prevention mechanism using the PN models in order to increase its efficiency in the system development.

Automated summary

This paper explores using a Petri net to create a visual model of the MapReduce framework and analyze its reachability property, demonstrating the feasibility of a real big data analysis system and proposing an error prevention mechanism to increase development efficiency.