Fluid Petri Nets for the Performance Evaluation of MapReduce Applications

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.