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 and Spark Applications, in Performance Evaluation Review, (Taormina, 25-28 October 2016), ACM, New York 2017:44 23-36. [10.1145/3092819.3092824] [http://hdl.handle.net/10807/202860]

Fluid Petri Nets for the Performance Evaluation of MapReduce and Spark Applications

Barbierato, Enrico
Penultimo
Software
;
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.
Inglese
Performance Evaluation Review
10th EAI International Conference on Performance Evaluation Methodologies and Tools
Taormina
25-ott-2016
28-ott-2016
978-1-4503-5846-0
ACM
Gianniti, E., Rizzi, A. M., Barbierato, E., Gribaudo, M., Ardagna, D., Fluid Petri Nets for the Performance Evaluation of MapReduce and Spark Applications, in Performance Evaluation Review, (Taormina, 25-28 October 2016), ACM, New York 2017:44 23-36. [10.1145/3092819.3092824] [http://hdl.handle.net/10807/202860]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/202860
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