Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management
The ISP (Internet Service Provider) industry relies heavily on internet traffic forecasting (ITF) for long-term business strategy planning and proactive network management. Effective ITF frameworks are necessary to manage these networks and prevent network congestion and over-provisioning. This stud...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/6/1871 |
| _version_ | 1797239398312443904 |
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| author | Sajal Saha Anwar Haque Greg Sidebottom |
| author_facet | Sajal Saha Anwar Haque Greg Sidebottom |
| author_sort | Sajal Saha |
| collection | DOAJ |
| description | The ISP (Internet Service Provider) industry relies heavily on internet traffic forecasting (ITF) for long-term business strategy planning and proactive network management. Effective ITF frameworks are necessary to manage these networks and prevent network congestion and over-provisioning. This study introduces an ITF model designed for proactive network management. It innovatively combines outlier detection and mitigation techniques with advanced gradient descent and boosting algorithms, including Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), CatBoost Regressor (CBR), and Stochastic Gradient Descent (SGD). In contrast to traditional methods that rely on synthetic datasets, our model addresses the problems caused by real aberrant ISP traffic data. We evaluated our model across varying forecast horizons—six, nine, and twelve steps—demonstrating its adaptability and superior predictive accuracy compared to traditional forecasting models. The integration of the outlier detection and mitigation module significantly enhances the model’s performance, ensuring robust and accurate predictions even in the presence of data volatility and anomalies. To guarantee that our suggested model works in real-world situations, our research is based on an extensive experimental setup that uses real internet traffic monitoring from high-speed ISP networks. |
| first_indexed | 2024-04-24T17:50:54Z |
| format | Article |
| id | doaj.art-b66ee2b6c4cc48ccb04d7447e7a51fe9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T17:50:54Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b66ee2b6c4cc48ccb04d7447e7a51fe92024-03-27T14:04:00ZengMDPI AGSensors1424-82202024-03-01246187110.3390/s24061871Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network ManagementSajal Saha0Anwar Haque1Greg Sidebottom2Department of Computer Science, University of Northern British Columbia, Prince George, BC V2N 4Z9, CanadaDepartment of Computer Science, Western University, London, ON N6A 3K7, CanadaJuniper Networks, Kanata, ON K2K 3E7, CanadaThe ISP (Internet Service Provider) industry relies heavily on internet traffic forecasting (ITF) for long-term business strategy planning and proactive network management. Effective ITF frameworks are necessary to manage these networks and prevent network congestion and over-provisioning. This study introduces an ITF model designed for proactive network management. It innovatively combines outlier detection and mitigation techniques with advanced gradient descent and boosting algorithms, including Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), CatBoost Regressor (CBR), and Stochastic Gradient Descent (SGD). In contrast to traditional methods that rely on synthetic datasets, our model addresses the problems caused by real aberrant ISP traffic data. We evaluated our model across varying forecast horizons—six, nine, and twelve steps—demonstrating its adaptability and superior predictive accuracy compared to traditional forecasting models. The integration of the outlier detection and mitigation module significantly enhances the model’s performance, ensuring robust and accurate predictions even in the presence of data volatility and anomalies. To guarantee that our suggested model works in real-world situations, our research is based on an extensive experimental setup that uses real internet traffic monitoring from high-speed ISP networks.https://www.mdpi.com/1424-8220/24/6/1871anomaly detectiongradient boostinggradient descenttraffic forecasttraffic predictionmachine learning |
| spellingShingle | Sajal Saha Anwar Haque Greg Sidebottom Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management Sensors anomaly detection gradient boosting gradient descent traffic forecast traffic prediction machine learning |
| title | Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management |
| title_full | Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management |
| title_fullStr | Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management |
| title_full_unstemmed | Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management |
| title_short | Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management |
| title_sort | multi step internet traffic forecasting models with variable forecast horizons for proactive network management |
| topic | anomaly detection gradient boosting gradient descent traffic forecast traffic prediction machine learning |
| url | https://www.mdpi.com/1424-8220/24/6/1871 |
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