Network traffic prediction based on PSO-LightGBM-TM
Network traffic prediction is critical in wireless network management by allowing a good estimate of the traffic trend, which is also an important approach for detecting traffic anomalies in order to enhance network security. Deep-learning-based method has been widely adopted to predict network traf...
Saved in:
Main Authors: | , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/180314 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-180314 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1803142024-10-11T02:44:29Z Network traffic prediction based on PSO-LightGBM-TM Li, Feng Nie, Wei Lam, Kwok-Yan Wang, Li College of Computing and Data Science School of Computer Science and Engineering Strategic Centre for Research in Privacy-Preserving Technologies & Systems (SCRIPTS) Computer and Information Science Network traffic prediction Machine learning Network traffic prediction is critical in wireless network management by allowing a good estimate of the traffic trend, which is also an important approach for detecting traffic anomalies in order to enhance network security. Deep-learning-based method has been widely adopted to predict network traffic matrix (TM) though with the main drawbacks in high complexity and low efficiency. In this paper, we propose a traffic prediction model based on Particle Swarm Optimization (PSO) and LightGBM (PSO-LightGBM-TM), which optimizes the LightGBM parameters for each network flow by PSO so that LightGBM can adapt to each of the network traffic flow. Compared with existing commonly used deep learning models, our model has a more straightforward structure and yet outperforms existing deep learning models. Sufficient comparison tests on three real network traffic datasets, Abilene, GÉANT, and CERNET have been conducted, and the results show that our model provides more accurate results and higher prediction efficiency. Info-communications Media Development Authority (IMDA) National Research Foundation (NRF) This research is supported by the National Research Foundation, Singapore under its Strategic Capability Research Centres Funding Initiative and Infocomm Media Development Authority under its Future Communications Research & Development Programme. 2024-10-11T02:44:29Z 2024-10-11T02:44:29Z 2024 Journal Article Li, F., Nie, W., Lam, K. & Wang, L. (2024). Network traffic prediction based on PSO-LightGBM-TM. Computer Networks, 254, 110810-. https://dx.doi.org/10.1016/j.comnet.2024.110810 1389-1286 https://hdl.handle.net/10356/180314 10.1016/j.comnet.2024.110810 2-s2.0-85204357326 254 110810 en Computer Networks © 2024 Elsevier B.V. All rights reserved. |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Computer and Information Science Network traffic prediction Machine learning |
spellingShingle |
Computer and Information Science Network traffic prediction Machine learning Li, Feng Nie, Wei Lam, Kwok-Yan Wang, Li Network traffic prediction based on PSO-LightGBM-TM |
description |
Network traffic prediction is critical in wireless network management by allowing a good estimate of the traffic trend, which is also an important approach for detecting traffic anomalies in order to enhance network security. Deep-learning-based method has been widely adopted to predict network traffic matrix (TM) though with the main drawbacks in high complexity and low efficiency. In this paper, we propose a traffic prediction model based on Particle Swarm Optimization (PSO) and LightGBM (PSO-LightGBM-TM), which optimizes the LightGBM parameters for each network flow by PSO so that LightGBM can adapt to each of the network traffic flow. Compared with existing commonly used deep learning models, our model has a more straightforward structure and yet outperforms existing deep learning models. Sufficient comparison tests on three real network traffic datasets, Abilene, GÉANT, and CERNET have been conducted, and the results show that our model provides more accurate results and higher prediction efficiency. |
author2 |
College of Computing and Data Science |
author_facet |
College of Computing and Data Science Li, Feng Nie, Wei Lam, Kwok-Yan Wang, Li |
format |
Article |
author |
Li, Feng Nie, Wei Lam, Kwok-Yan Wang, Li |
author_sort |
Li, Feng |
title |
Network traffic prediction based on PSO-LightGBM-TM |
title_short |
Network traffic prediction based on PSO-LightGBM-TM |
title_full |
Network traffic prediction based on PSO-LightGBM-TM |
title_fullStr |
Network traffic prediction based on PSO-LightGBM-TM |
title_full_unstemmed |
Network traffic prediction based on PSO-LightGBM-TM |
title_sort |
network traffic prediction based on pso-lightgbm-tm |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/180314 |
_version_ |
1814047296452558848 |