Long short-term memory recurrent neural networks models to forecast the resource usage of MapReduce applications

Li, Yangyuan and Do, Tien Van: Long short-term memory recurrent neural networks models to forecast the resource usage of MapReduce applications. Conference of PhD Students in Computer Science, (11). pp. 176-178. (2018)

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Abstract

The forecasting of the resource usage of MapReduce applications plays an important role in the operation of cloud infrastructure. In this paper, we apply long short-term memory recurrent neural networks to predict the resource usage of three representative MapReduce applications. The Results show that the Long Short-term Memory Recurrent Neural Networks models perform higher prediction accuracy than persistence ones. Predictions of other usage parameters show similar accuracy with persistence one. The improper configuration parameters of Long Short-term Memory Recurrent Neural Networks possibly result in few of worse prediction.

Item Type: Article
Event Title: Conference of PhD students in computer science (11.) (2018) (Szeged)
Journal or Publication Title: Conference of PhD Students in Computer Science
Date: 2018
Volume: 11
Page Range: pp. 176-178
Uncontrolled Keywords: MapReduce, Programozás, Számítástechnika
Additional Information: Bibliogr.: 178. p. ; összefoglalás angol nyelven
Date Deposited: 2019. Nov. 04. 14:47
Last Modified: 2019. Nov. 04. 14:47
URI: http://acta.bibl.u-szeged.hu/id/eprint/61797

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