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Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps fo...

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Autores principales: Hassan, Mohamed Khalafalla, Syed Ariffin, Sharifah Hafizah, Ghazali, N. Effiyana, Hamad, Mutaz, Hamdan, Mosab, Hamdi, Monia, Hamam, Habib, Khan, Suleman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103727/
https://www.ncbi.nlm.nih.gov/pubmed/35591282
http://dx.doi.org/10.3390/s22093592
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author Hassan, Mohamed Khalafalla
Syed Ariffin, Sharifah Hafizah
Ghazali, N. Effiyana
Hamad, Mutaz
Hamdan, Mosab
Hamdi, Monia
Hamam, Habib
Khan, Suleman
author_facet Hassan, Mohamed Khalafalla
Syed Ariffin, Sharifah Hafizah
Ghazali, N. Effiyana
Hamad, Mutaz
Hamdan, Mosab
Hamdi, Monia
Hamam, Habib
Khan, Suleman
author_sort Hassan, Mohamed Khalafalla
collection PubMed
description Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. Moreover, the proposed framework can dynamically react to all the changes occurring in the data series. Backbone traffic was used to validate the proposed method. As a result, the forecasting accuracy improved significantly with the proposed framework and with minimal data loss from the smoothing process. The results showed that the hybrid moving average LSTM (MLSTM) achieved the most remarkable improvement in the training and testing forecasts, with 28% and 24% for long-term evolution (LTE) time series and with 35% and 32% for the multiprotocol label switching (MPLS) time series, respectively, while robust locally weighted scatter plot smoothing and LSTM (RLWLSTM) achieved the most significant improvement for upstream traffic with 45%; moreover, the dynamic learning framework achieved improvement percentages that can reach up to 100%.
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spelling pubmed-91037272022-05-14 Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts Hassan, Mohamed Khalafalla Syed Ariffin, Sharifah Hafizah Ghazali, N. Effiyana Hamad, Mutaz Hamdan, Mosab Hamdi, Monia Hamam, Habib Khan, Suleman Sensors (Basel) Article Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. Moreover, the proposed framework can dynamically react to all the changes occurring in the data series. Backbone traffic was used to validate the proposed method. As a result, the forecasting accuracy improved significantly with the proposed framework and with minimal data loss from the smoothing process. The results showed that the hybrid moving average LSTM (MLSTM) achieved the most remarkable improvement in the training and testing forecasts, with 28% and 24% for long-term evolution (LTE) time series and with 35% and 32% for the multiprotocol label switching (MPLS) time series, respectively, while robust locally weighted scatter plot smoothing and LSTM (RLWLSTM) achieved the most significant improvement for upstream traffic with 45%; moreover, the dynamic learning framework achieved improvement percentages that can reach up to 100%. MDPI 2022-05-09 /pmc/articles/PMC9103727/ /pubmed/35591282 http://dx.doi.org/10.3390/s22093592 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hassan, Mohamed Khalafalla
Syed Ariffin, Sharifah Hafizah
Ghazali, N. Effiyana
Hamad, Mutaz
Hamdan, Mosab
Hamdi, Monia
Hamam, Habib
Khan, Suleman
Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts
title Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts
title_full Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts
title_fullStr Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts
title_full_unstemmed Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts
title_short Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts
title_sort dynamic learning framework for smooth-aided machine-learning-based backbone traffic forecasts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103727/
https://www.ncbi.nlm.nih.gov/pubmed/35591282
http://dx.doi.org/10.3390/s22093592
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