Long short-term memory recurrent neural networks models to forecast the resource usage of MapReduce applications
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 th...
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| Dokumentumtípus: | Könyv része |
| Megjelent: |
2018
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| Sorozat: | Conference of PhD Students in Computer Science
11 |
| Kulcsszavak: | MapReduce, Programozás, Számítástechnika |
| Online Access: | http://acta.bibl.u-szeged.hu/61797 |
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| 040 | |a SZTE Egyetemi Kiadványok Repozitórium |b hun | ||
| 041 | |a zxx | ||
| 100 | 1 | |a Li Yangyuan | |
| 245 | 1 | 0 | |a Long short-term memory recurrent neural networks models to forecast the resource usage of MapReduce applications |h [elektronikus dokumentum] / |c Li Yangyuan |
| 260 | |c 2018 | ||
| 300 | |a 176-178 | ||
| 490 | 0 | |a Conference of PhD Students in Computer Science |v 11 | |
| 520 | 3 | |a 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. | |
| 695 | |a MapReduce, Programozás, Számítástechnika | ||
| 700 | 0 | 1 | |a Do Tien Van |e aut |
| 710 | |a Conference of PhD students in computer science (11.) (2018) (Szeged) | ||
| 856 | 4 | 0 | |u http://acta.bibl.u-szeged.hu/61797/1/cscs_2018_189-191.pdf |z Dokumentum-elérés |