A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks
This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conduc...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/11/4997 |
| _version_ | 1797596792780488704 |
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| author | Francisco Rau Ismael Soto David Zabala-Blanco Cesar Azurdia-Meza Muhammad Ijaz Sunday Ekpo Sebastian Gutierrez |
| author_facet | Francisco Rau Ismael Soto David Zabala-Blanco Cesar Azurdia-Meza Muhammad Ijaz Sunday Ekpo Sebastian Gutierrez |
| author_sort | Francisco Rau |
| collection | DOAJ |
| description | This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conducted in the telecommunications industry to address the problem of energy efficiency in data centers. The case study involved comparing four recurrent and sequential neural networks, including recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and online sequential extreme learning machine (OS-ELM), to determine the best network in terms of prediction accuracy and computational time. The results show that OS-ELM outperformed the other networks in both accuracy and computational efficiency. The simulation was applied to real traffic data and showed potential energy savings of up to 12.2% in a single day. This highlights the importance of energy efficiency and the potential for the methodology to be applied to other industries. The methodology can be further developed as technology and data continue to advance, making it a promising solution for a wide range of prediction problems. |
| first_indexed | 2024-03-11T02:58:02Z |
| format | Article |
| id | doaj.art-c8fdd341c4404f108b946a856cd0991f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:58:02Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c8fdd341c4404f108b946a856cd0991f2023-11-18T08:31:06ZengMDPI AGSensors1424-82202023-05-012311499710.3390/s23114997A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider NetworksFrancisco Rau0Ismael Soto1David Zabala-Blanco2Cesar Azurdia-Meza3Muhammad Ijaz4Sunday Ekpo5Sebastian Gutierrez6CIMTT, Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, ChileCIMTT, Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, ChileDepartment of Computer Science and Industry, Universidad Católica del Maule, Talca 3480112, ChileDepartment of Electrical Engineering, Universidad de Chile, Santiago 8370451, ChileDepartment of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M1 5GD, UKDepartment of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M1 5GD, UKFaculty of Engineering, Universidad Autónoma de Chile, Santiago 7500912, ChileThis paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conducted in the telecommunications industry to address the problem of energy efficiency in data centers. The case study involved comparing four recurrent and sequential neural networks, including recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and online sequential extreme learning machine (OS-ELM), to determine the best network in terms of prediction accuracy and computational time. The results show that OS-ELM outperformed the other networks in both accuracy and computational efficiency. The simulation was applied to real traffic data and showed potential energy savings of up to 12.2% in a single day. This highlights the importance of energy efficiency and the potential for the methodology to be applied to other industries. The methodology can be further developed as technology and data continue to advance, making it a promising solution for a wide range of prediction problems.https://www.mdpi.com/1424-8220/23/11/4997energy efficiencymachine learningtelecom services operatortraffic prediction |
| spellingShingle | Francisco Rau Ismael Soto David Zabala-Blanco Cesar Azurdia-Meza Muhammad Ijaz Sunday Ekpo Sebastian Gutierrez A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks Sensors energy efficiency machine learning telecom services operator traffic prediction |
| title | A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks |
| title_full | A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks |
| title_fullStr | A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks |
| title_full_unstemmed | A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks |
| title_short | A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks |
| title_sort | novel traffic prediction method using machine learning for energy efficiency in service provider networks |
| topic | energy efficiency machine learning telecom services operator traffic prediction |
| url | https://www.mdpi.com/1424-8220/23/11/4997 |
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