Li, Yangyuan and Do, Tien Van: Long short-term memory recurrent neural networks models to forecast the resource usage of MapReduce applications. In: 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 |
|---|---|
| Journal or Publication Title: | Conference of PhD Students in Computer Science |
| Date: | 2018 |
| Volume: | 11 |
| Page Range: | pp. 176-178 |
| Event Title: | Conference of PhD students in computer science (11.) (2018) (Szeged) |
| 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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