Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts release_fyh7wf2qdvha3gro4uabr25rfq

by Mohamed Khalafalla Hassan, Sharifah Hafizah Syed Ariffin, Nurzal Effiyana Ghazali, Mutaz Hamad, Mosab Hamdan, Monia Hamdi, Habib Hamam, Suleman Khan

Published in Sensors by MDPI AG.

2022   Volume 22, Issue 9, p3592

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%.
In application/xml+jats format

Archived Files and Locations

application/pdf  9.9 MB
file_5bhbxpvdsrcu5fjo2dhwhoar6m
mdpi-res.com (publisher)
web.archive.org (webarchive)

Web Captures

https://www.mdpi.com/1424-8220/22/9/3592/htm
2022-09-17 05:27:27 | 63 resources
webcapture_du2w6hp6kbannkgvjho77qba3y
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2022-05-09
Language   en ?
DOI  10.3390/s22093592
PubMed  35591282
PMC  PMC9103727
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  1424-8220
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: e7c75498-ff45-4b3b-a462-0949c3474243
API URL: JSON