大型IP网络流量矩阵分析预测的探讨研究

doi:10.12305/j.issn.1001-506X.2024.06.35

Abstract

Abstract:

Efficient and accurate analysis and prediction of traffic flow direction for Internet protocol (IP) network are the basis of network planning and construction. By deploying a traffic collection and analysis system, operators can easily obtain comprehensive historical data such as network total traffic, node traffic, and node directional traffic, which provides key inputs for traffic analysis and prediction. Methods of traffic analysis and prediction for IP network are generally divided into two categories: traditional statistical model and neural network model. The NeuralProphet model proposed in recent years has been widely applied due to its combination of the advantages of the above models. It is the first time to directly predict the origin-destination traffic flow of large-scale carrier-grade IP network based on the NeuralProphet model, and adopts the improved loss function to optimize model training. The prediction results show that the NeuralProphet model can predict traffic matrix of IP network more scientifically and accurately, and the overall prediction accuracy was improved by 8.7%. Meanwhile, the model has better scalability and robustness, which can better meet the actual needs of IP network planning and maintenance.

Key words: traffic matrix, origin-destination traffic flow, node traffic, prediction model, auto-regression

CLC Number:

TN915

Xuan WEI, Zhihua LIU, Qing LI, Xiaoming HE, Junya HUANG. Research on analysis and prediction of traffic matrix for large-scale IP network[J]. Systems Engineering and Electronics, 2024, 46(6): 2164-2173.

Figures/Tables 14

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节点	节点
节点	N1	N2	N3	…	N14	N15	N16
N1	437.32	28.08	14.98	…	21.01	6.69	52.06
N2	4.49	60.76	6.86	…	6.12	6.11	16.32
N3	21.62	27.64	306.76	…	16.01	48.22	46.34
				$\ddots$			
N14	17.08	5.69	17.18	…	805.44	8.75	256.11
N15	7.81	15.78	86.89	…	10.98	206.89	23.53
N16	51.95	18.81	57.34	…	323.07	28.82	2 337.57

时间	节点
时间	N1-N1	N1-N2	N1-N3	…	N16-N14	N16-N15	N16-N16
2015年5月	420.31	31.77	13.73	…	355.62	29.90	2 220.03
2015年6月	437.32	28.08	14.98	…	323.07	28.82	2 337.57
2015年7月	484.97	24.85	14.03	…	339.89	28.81	2 642.86
				$\ddots$			
2023年1月	2 985.36	357.42	254.48	…	504.20	93.56	3 098.55
2023年2月	2 904.61	347.41	268.89	…	511.16	94.60	3 314.75
2023年3月	2 690.06	324.63	285.16	…	594.21	99.46	3 267.70

模型	评价指标
模型	RMSE(Gb/s)	MAE(Gb/s)	MAPE/%
ARIMA	246.7	100.9	53.3
Prophet	124.6	56.9	29.2
NeuralProphet	75.1	33.1	20.5

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Research on analysis and prediction of traffic matrix for large-scale IP network

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