基于聚类分析算法和优化支持向量机的无线网络流量预测

doi:10.11896/jsjkx.190800075

摘要/Abstract

摘要： 为了解决当前无线网络流量预测过程存在的一些问题, 以提高无线网络流量的预测精度为目标, 提出基于聚类分析算法和优化支持向量机的无线网络流量预测模型。首先, 采集无线网络流量数据集, 并采用聚类分析算法构建训练样本集合;然后, 采用支持向量机对无线网络流量训练样本进行学习, 并引入布谷鸟搜索算法对支持向量参数进行优化, 从而建立无线网络流量预测模型;最后, 通过具体无线网络流量预测实例分析模型的有效性。结果表明, 所提模型的无线网络流量预测精度高, 提升了无线网络流量建模效率, 而且其无线网络流量预测效果要优于当前经典无线网络流量预测模型, 具有比较显著的优越性。

关键词: 布谷鸟搜索算法, 聚类分析算法, 流量预测, 无线网络, 支持向量机

Abstract: In order to solve the problems existing in the current wireless network traffic prediction process and improve the accuracy of wireless network traffic prediction, a wireless network traffic prediction model based on clustering analysis algorithm and optimized support vector machine is proposed.Firstly, data sets of wireless network traffic are collected and clustering analysis algorithm is used to construct the training sample set.And then, support vector machine is used to learn the training samples of wireless network traffic, and cuckoo search algorithms is introduced to optimize the parameters of support vector.Thus, the prediction model of wireless network traffic is established.Finally, the effectiveness of the model is analyzed through a specific example of wireless network traffic prediction.The results show that the proposed model has high prediction accuracy, improves the efficiency of wireless network traffic modeling, and the prediction effect of wireless network traffic is better than the current classical wireless network traffic prediction models, which has more significant advantages.

Key words: Clustering analysis algorithm, Cuckoo search algorithms, Support vector machine, Traffic prediction, Wireless network

中图分类号:

TP391

曹素娥, 杨泽民. 基于聚类分析算法和优化支持向量机的无线网络流量预测[J]. 计算机科学, 2020, 47(8): 319-322. https://doi.org/10.11896/jsjkx.190800075

CAO Su-e, YANG Ze-min. Prediction of Wireless Network Traffic Based on Clustering Analysis and Optimized Support Vector Machine[J]. Computer Science, 2020, 47(8): 319-322. https://doi.org/10.11896/jsjkx.190800075

参考文献

[1]CINIC.The 41st China statistical report on Internet development [R].Beijing:China Internet Network Information Center, 2018.
[2]VELAN P, CERMAK M, CELEDA P, et al.A survey of methods for encrypted traffic classification and analysis[J].International Journal of Network Management, 2015, 25(5):355-374.
[3]DU Z, MA L P, SUN G Z.Network Traffic Anomaly Detection Based on Wavelet Analysis [J].Computer Science, 2019, 46(8):178-182.
[4]GUO J, YU Y B, YANG C Y.Multi-step network traffic prediction based on full attention mechanism [J].Signal Processing, 2019, 35(5):758-767.
[5]CHEN X, TANG J Y.Internet of Things traffic prediction mo-del based on Bayesian and causal ridge regression [J].Journal of Sichuan University (Natural Science Edition), 2018, 55 (5):965-970.
[6]GUO F, CHEN L, YANG Z W.Real-time traffic prediction of large-scale IP backbone network based on MGU [J].Journal of Shandong University (Engineering Science), 2019, 49(2):88-95.
[7]ACETO G, CIUONZO D, MONTIERI A, et al.Mobile encrypted traffic classification using deep learning[C]∥2018 Network Traffic Measurement and Analysis Conference (TMA).IEEE, 2018:1-8.
[8]GE S C, LIU X F, ZHOU F.Train information transmission network traffic modeling and prediction of CRH2 EMU [J].Computer Science, 2017, 44(10):91-95, 126.
[9]LI R, ZHAO Z, ZHENG J, et al.The learning and prediction of application-level traffic data in cellular networks [J].IEEE Transactions on Wireless Communications, 2017, 16(6):3899-3912.
[10]LIN Z D, LV H H.Network traffic prediction mechanism based on wavelet coefficient perception [J].Journal of Terahertz Science and Electronic Information Technology, 2019, 17(1):131-135.
[11]LIU Y, LI W, LI Y C.Network traffic classification using k-means clustering[C]∥International Multi-symposiums on Computer & Computational Sciences.2007.
[12]CHEN Z, LIU Z, PENG L, et al.A novel semi-supervised lear-ning method for Internet application identification [J].Soft Computing, 2017, 21(8):1963-1975.
[13]LOTFOLLAHI M, SIAVOSHANI M J, ZADE R S H, et al.Deep packet:A novel approach for encrypted traffic classification using deep learning [J].Soft Computing, 2017, 28(9):1-14.
[14]LI Y Q, H Y, SUN X C.Network traffic prediction model based on deep confidence echo state network [J].Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition), 2018, 38(5):85-90.
[15]WANG K.Research on network traffic prediction based oncloud computing and extreme learning machine [J].Journal of Shandong Agricultural University (Natural Science Edition), 2018, 49(4):632-635.
[16]LIU K.Chaotic prediction of network traffic based on particle swarm optimization support vector machine [J].Modern Electronics Technique, 2019, 42(2):120-123.
[17]ZHANG C, PAUL P.Long-term mobile traffic forecasting using deep spatio-temporal neural networks[C]∥Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing.Angeles:ACM, 2018:231-240.
[18]NAREJO S, PASERO E.An Application of Internet Traffic Prediction with Deep Neural Network [J].Multidisciplinary Approaches to Neural Computing, 2018, 69(1):139-149.

相关文章 15

[1]	单晓英, 任迎春. 基于改进麻雀搜索优化支持向量机的渔船捕捞方式识别 Fishing Type Identification of Marine Fishing Vessels Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm 计算机科学, 2022, 49(6A): 211-216. https://doi.org/10.11896/jsjkx.220300216
[2]	陈景年. 一种适于多分类问题的支持向量机加速方法 Acceleration of SVM for Multi-class Classification 计算机科学, 2022, 49(6A): 297-300. https://doi.org/10.11896/jsjkx.210400149
[3]	田真真, 蒋维, 郑炳旭, 孟利民. 基于服务器集群的负载均衡优化调度算法 Load Balancing Optimization Scheduling Algorithm Based on Server Cluster 计算机科学, 2022, 49(6A): 639-644. https://doi.org/10.11896/jsjkx.210800071
[4]	侯夏晔, 陈海燕, 张兵, 袁立罡, 贾亦真. 一种基于支持向量机的主动度量学习算法 Active Metric Learning Based on Support Vector Machines 计算机科学, 2022, 49(6A): 113-118. https://doi.org/10.11896/jsjkx.210500034
[5]	邢云冰, 龙广玉, 胡春雨, 忽丽莎. 基于SVM的类别增量人体活动识别方法 Human Activity Recognition Method Based on Class Increment SVM 计算机科学, 2022, 49(5): 78-83. https://doi.org/10.11896/jsjkx.210400024
[6]	高志宇, 王天荆, 汪悦, 沈航, 白光伟. 基于生成对抗网络的5G网络流量预测方法 Traffic Prediction Method for 5G Network Based on Generative Adversarial Network 计算机科学, 2022, 49(4): 321-328. https://doi.org/10.11896/jsjkx.210300240
[7]	赵罗成, 屈志昊, 谢在鹏. 面向多层无线边缘环境下的联邦学习通信优化的研究 Study on Communication Optimization of Federated Learning in Multi-layer Wireless Edge Environment 计算机科学, 2022, 49(3): 39-45. https://doi.org/10.11896/jsjkx.210800054
[8]	武玉坤, 李伟, 倪敏雅, 许志骋. 单类支持向量机融合深度自编码器的异常检测模型 Anomaly Detection Model Based on One-class Support Vector Machine Fused Deep Auto-encoder 计算机科学, 2022, 49(3): 144-151. https://doi.org/10.11896/jsjkx.210100142
[9]	侯春萍, 赵春月, 王致芃. 基于自反馈最优子类挖掘的视频异常检测算法 Video Abnormal Event Detection Algorithm Based on Self-feedback Optimal Subclass Mining 计算机科学, 2021, 48(7): 199-205. https://doi.org/10.11896/jsjkx.200800146
[10]	郭福民, 张华, 胡瑢华, 宋岩. 一种基于表面肌电信号的腕部肌力估计方法研究 Study on Method for Estimating Wrist Muscle Force Based on Surface EMG Signals 计算机科学, 2021, 48(6A): 317-320. https://doi.org/10.11896/jsjkx.200600021
[11]	何权奇, 余飞鸿. 面向无线网络相机的低功耗架构研究综述 Review of Low Power Architecture for Wireless Network Cameras 计算机科学, 2021, 48(6A): 369-373. https://doi.org/10.11896/jsjkx.201100099
[12]	宋元隆, 吕光宏, 王桂芝, 贾吾财. 基于图卷积神经网络的SDN网络流量预测 SDN Traffic Prediction Based on Graph Convolutional Network 计算机科学, 2021, 48(6A): 392-397. https://doi.org/10.11896/jsjkx.200800090
[13]	彭大川, 杨喜敏, 唐菀, 张潇, 范垒. 软件定义无线网络中双网络通道互备无线接入点切换方案 Dual-NIC Mutual Backup Scheme for Access Point Handoff in Software Defined Wireless Networks 计算机科学, 2021, 48(6A): 427-431. https://doi.org/10.11896/jsjkx.201000022
[14]	卓雅倩, 欧博. 噪声环境下的人脸防伪识别算法研究 Face Anti-spoofing Algorithm for Noisy Environment 计算机科学, 2021, 48(6A): 443-447. https://doi.org/10.11896/jsjkx.200900207
[15]	郭奕杉, 刘漫丹. 基于时空轨迹数据的异常检测 Anomaly Detection Based on Spatial-temporal Trajectory Data 计算机科学, 2021, 48(6A): 213-219. https://doi.org/10.11896/jsjkx.201100193

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed