Universiti Teknologi Malaysia Institutional Repository

Dynamic learning framework for smooth-aided machine-learning-based backbone traffic forecasts

Hassan, Mohamed Khalafalla and Syed Ariffin, Sharifah Hafizah and Ghazali, N. Effiyana and Hamad, Mutaz and Hamdan, Mosab and Hamdi, Monia and Hamam, Habib and Suleman Khan, Suleman Khan (2022) Dynamic learning framework for smooth-aided machine-learning-based backbone traffic forecasts. Sensors, 22 (9). pp. 1-27. ISSN 1424-8220

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Official URL: http://dx.doi.org/10.3390/s22093592

Abstract

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%.

Item Type:Article
Uncontrolled Keywords:dynamic learning, local smoothing, LSTM, slice, traffic forecast
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Faculty of Engineering - School of Electrical
ID Code:104017
Deposited By: Yanti Mohd Shah
Deposited On:14 Jan 2024 00:40
Last Modified:14 Jan 2024 00:40

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