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.
Gianniti, E., Rizzi, A. M., Barbierato, E., Gribaudo, M., Ardagna, D., Fluid Petri nets for the performance evaluation of MapReduce applications, in ValueTools 2016 - 10th EAI International Conference on Performance Evaluation Methodologies and Tools, (Taormina, 25-28 October 2016), Association for Computing Machinery, New York 2017: 243-250. [10.4108/eai.25-10-2016.2267025] [http://hdl.handle.net/10807/202868]
Fluid Petri nets for the performance evaluation of MapReduce applications
Barbierato, Enrico;
2017
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.
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