基于MGU的大规模IP骨干网络实时流量预测

doi:10.6040/j.issn.1672-3961.0.2018.342

山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (2): 88-95.doi: 10.6040/j.issn.1672-3961.0.2018.342

基于MGU的大规模IP骨干网络实时流量预测

郭芳¹(),陈蕾^1,^2,³,杨子文¹

1. 南京邮电大学计算机学院，江苏南京 210023
2. 江苏省无线传感网高技术研究重点实验室，江苏南京 210023
3. 南京航空航天大学计算机科学与技术学院，江苏南京 210016

收稿日期:2018-08-13 出版日期:2019-04-20 发布日期:2019-04-19
作者简介:郭芳(1992—)，女，云南建水人，硕士研究生，主要研究方向为机器学习与网络流量预测. E-mail:lighter_around@163.com
基金资助:
江苏省自然科学基金(BK20161516);中国博士后科学基金(2015M581794);国家自然科学基金(61872190)

Real-time traffic prediction based on MGU for large-scale IP backbone networks

Fang GUO¹(),Lei CHEN^1,^2,³,Ziwen YANG¹

1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, Jiangsu, China
2. Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, Jiangsu, China
3. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China

Received:2018-08-13 Online:2019-04-20 Published:2019-04-19
Supported by:
江苏省自然科学基金(BK20161516);中国博士后科学基金(2015M581794);国家自然科学基金(61872190)

摘要/Abstract

摘要：

为克服长短时记忆网络(long short-term memory, LSTM)计算成本相当大的弊端，提出基于最小门控单元(minimal gated unit, MGU)的大规模IP骨干网络实时流量预测方法。试验结果表明，与基于LSTM的流量预测方法相比，该方法以较少的模型训练时间获得了相当甚至略优的流量预测性能，在流量预测精度和实时性方面也优于已有的前馈神经网络(feed forward neural network, FFNN)和门控循环单元神经网络(gated recurrent unit, GRU)方法。

关键词: 网络流量预测, 大规模IP骨干网, 循环神经网络, LSTM, MGU

Abstract:

In order to overcome the shortcomings of long short-term memory (LSTM) computing cost, a real-time traffic prediction method based on minimum gated unit (MGU) for large-scale IP backbone networks was proposed. The experimental results showed that compared with the LSTM-based traffic prediction method, the proposed method achieved fairly or even better traffic prediction performance with less model training time, meanwhile it outperformed the most advanced feed forward neural network (FFNN), LSTM and gated recurrent unit(GRU) in terms of prediction accuracy and real-time performance.

Key words: network traffic prediction, large-scale IP backbone networks, recurrent neural network, long short-term memory, minimal gated units

中图分类号:

TP393

郭芳,陈蕾,杨子文. 基于MGU的大规模IP骨干网络实时流量预测[J]. 山东大学学报 (工学版), 2019, 49(2): 88-95.

Fang GUO,Lei CHEN,Ziwen YANG. Real-time traffic prediction based on MGU for large-scale IP backbone networks[J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 88-95.

图/表 10

图1

图2

图3

图4

图5

图6

表1

图7

图8

图9

参考文献 23

1	QU H , MA W T , ZHAO J H , et al. Prediction method for network traffic based on maximum correntropy criterion[J]. China Communications, 2013, 10 (1): 134- 145. doi: 10.1109/CC.2013.6457536
2	田中大, 李树江, 王艳红, 等. 高斯过程回归补偿ARIMA的网络流量预测[J]. 北京邮电大学学报, 2017, 40 (6): 65- 73.
	TIAN Zhongda , LI Shujiang , WANG Yanhong , et al. Network traffic prediction based on ARIMA with gaussian process regression compensation[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40 (6): 65- 73.
3	AZZOUNI A, PUJOLLE G. A long short-term memory recurrent neural network framework for network traffic matrix prediction[EB/OL]. (2017-06-08)[2018-04-15]. https://arxiv.org/abs/1705.05690.
4	LANER M , SVOBODA P , RUPP M . Parsimonious fitting of long-range dependent network traffic using A RMA models[J]. IEEE Communications Letters, 2013, 17 (12): 2368- 2371. doi: 10.1109/LCOMM.2013.102613.131853
5	YADAV R K , BALAKRISHNAN M . Comparative evaluation of ARIMA and ANFIS for modeling of wireless network traffic time series[J]. Eurasip Journal on Wireless Communications & Networking, 2014, 2014 (1): 8- 15.
6	KATRIS C , DASKALAKI S . Comparing forecasting approaches for Internet traffic[J]. Expert Systems with Applications, 2015, 42 (21): 8172- 8183. doi: 10.1016/j.eswa.2015.06.029
7	NIE L S , JIANG D D , GUO L , et al. Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks[J]. Journal of Network & Computer Applications, 2016, 76 (C): 16- 22.
8	LIANG Y , QIU L . Network traffic prediction based on SVR improved by chaos theory and ant colony optimization[J]. International Journal of Future Generation Communication & Networking, 2015, 8 (1): 484- 488.
9	Xiang C , Qu P , Qu X . Network traffic prediction based on MK-SVR[J]. Journal of Information & Computational Science, 2015, 12 (8): 3185- 3197.
10	HONG W C . Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting[J]. Neural Computing and Applications, 2012, 21 (3): 583- 593. doi: 10.1007/s00521-010-0456-7
11	LV Y , DUAN Y , KANG W , et al. Traffic flow prediction with big data: a deep learning approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16 (2): 865- 873.
12	江露琪, 孙文胜. 基于改进的BP神经网络的网络流量预测模型[J]. 通信技术, 2017, 50 (1): 68- 73.
	JIANG Luqi , SUN Wensheng . Research and implementation of network traffic prediction based on modified BP neural network[J]. Communication Technology, 2017, 50 (1): 68- 73.
13	SHAO H X, SOONG B H. Traffic flow prediction with Long Short-Term Memory Networks (LSTMs)[C]// Region 10 conference (TENCON 2016). Singapore: IEEE, 2017: 2986-2989.
14	LUO X , ZHOU W , WANG W , et al. Attention-based relation extraction with bidirectional gated recurrent unit and highway network in the analysis of geological data[J]. IEEE Access, 2018, 27 (6): 5705- 5715.
15	FU R, ZHANG Z, LI L. Using LSTM and GRU neural network methods for traffic flow prediction[C]//In Proceedings of the Youth Academic Annual Conference of Chinese Association of Automation (YAC). Wuhan: IEEE, 2016: 324-328.
16	ZHOU G B , WU J , ZHANG C L , et al. Minimal gated unit for recurrent neural networks[J]. International Journal of Automation and Computing, 2016, 13 (3): 226- 234. doi: 10.1007/s11633-016-1006-2
17	HOCHREITER S , SCHMIDHUBER J . Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780. doi: 10.1162/neco.1997.9.8.1735
18	BENGIO Y , SIMARD P , FRASCONI P . Learning long-term dependencies with gradient descent is difficult[J]. IEEE transactions on neural networks, 1994, 5 (2): 157- 166. doi: 10.1109/72.279181
19	CHUNG J, GULCEHRE C, CHO K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[EB/OL]. (2014-12-11)[2018-04-15]. https://arxiv.org/abs/1412.3555.
20	LAKHINA A , PAPAGIANNAKI K , CROVELLA M , et al. Structural analysis of network traffic flows[J]. Acm Sigmetrics Performance Evaluation Review, 2004, 32 (1): 61- 72. doi: 10.1145/1012888
21	JIANG D D , WANG X , GUO L , et al. Accurate estimation of large-scale IP traffic matrix[J]. AEU - International Journal of Electronics and Communications, 2011, 65 (1): 75- 86. doi: 10.1016/j.aeue.2010.02.008
22	UHLIG S , QUOITIN B , LEPROPRE J , et al. Providing public intradomain traffic matrices to the research community[J]. Acm Sigcomm Computer Communication Review, 2006, 36 (1): 83- 86. doi: 10.1145/1111322
23	KINGMA D P, BA J. Adam: a method for stochastic optimization[EB/OL]. (2017-01-30)[2018-04-15]. https://arxiv.org/abs/1412.6980.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

单元名称	#参数	平均MAE(10³)
FFNN	10 689	2.47
LSTM	49 985	2.10
GRU	37 505	1.95
MGU	25 025	1.84

[1]	廖南星,周世斌,张国鹏,程德强. 基于类激活映射-注意力机制的图像描述方法[J]. 山东大学学报 (工学版), 2020, 50(4): 28-34.
[2]	胡龙茂,胡学钢. 基于多维相似度和情感词扩充的相同产品特征识别[J]. 山东大学学报 (工学版), 2020, 50(2): 50-59.

基于MGU的大规模IP骨干网络实时流量预测

Real-time traffic prediction based on MGU for large-scale IP backbone networks

RichHTML

PDF (PC)

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 23

相关文章 2

多维度评价

本文评价

推荐阅读 10