基于 PSO-LightGBM-TM 的网络流量预测

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Feng Li , Wei Nie , Kwok-Yan Lam , Li Wang
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引用次数: 0

摘要

网络流量预测是无线网络管理的关键,它可以很好地估计流量趋势,也是检测流量异常以加强网络安全的重要方法。基于深度学习的方法已被广泛用于预测网络流量矩阵(TM),但其主要缺点是复杂度高、效率低。本文提出了一种基于粒子群优化(PSO)和LightGBM的流量预测模型(PSO-LightGBM-TM),通过PSO优化每个网络流量的LightGBM参数,使LightGBM能够适应每个网络流量。与现有的常用深度学习模型相比,我们的模型结构更简单,但性能却优于现有的深度学习模型。我们在 Abilene、GÉANT 和 CERNET 三个真实网络流量数据集上进行了充分的对比测试,结果表明我们的模型能提供更准确的结果和更高的预测效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.

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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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