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.

Context-aware data quality assessment for big data

“…The model aims at capturing the relation between the execution time and the dataset size as well as the configuration parameters. The authors investigated the issue also in [23], where a fluid Petri net has been employed to create a model able to envision MapReduce jobs execution time.…”