Long Short-Term Memory Networks for Server Workload Prediction-Reference-Cited by-同舟云学术

Long Short-Term Memory Networks for Server Workload Prediction

Published:2024-05-08 Issue: Volume: Page:1-7
ISSN:
Container-title:2024 International Conference on Intelligent Systems and Computer Vision (ISCV)
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Author:

El Yadari Meryeme¹,El Motaki Saloua²,Yahyaouy Ali³,Makany Philippe⁴,El Fazazy Khalid⁵,Gualous Hamid⁶,Le Masson Stéphane⁷

Affiliation:

1. Automatics and Cognitivism (LISAC) and University Laboratory of Applied Sciences of Cherbourg (LUSAC) Sidi Mohamed Ben Abdellah University Caen Normandy University,Laboratory of Computer Science, Signals,Fes,Morocco

2. Chouaib Doukkali University,Laboratory of Engineering Sciences for Energy (LabSIPE) National School of Applied Science,El Jadida,Morocco

3. Sidi Mohamed Ben Abdellah University and LaMSN - The House of Digital Sciences, USPN,LISAC Laboratory,Fes, Morocco Paris,France

4. Caen Normandy University,LUSAC Laboratory,Caen,France

5. Sidi Mohamed Ben Abdellah University,LISAC Laboratory,Fes,Morocco

6. Caen Normandy University,LUSAC Laboratory,Saint-lô,France

7. Orange Labs R&D,Lannion,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10620018/10620073/10620113.pdf?arnumber=10620113

Reference19 articles.

1. Workload prediction in cloud using artificial neural network and adaptive differential evolution

2. Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters

3. Workload and energy management of geo-distributed datacenters considering demand response programs

4. Holistic energy and failure aware workload scheduling in Cloud datacenters

5. A survey of challenges and solutions for the integration of renewable energy in datacenters