基于图神经网络的网络性能智能预测

doi:10.11959/j.issn.1000-0801.2022062

Abstract

Abstract:

There are some problems in the traditional network performance prediction technology, such as incomplete network state acquisition and poor accuracy of network performance evaluation.Combined with the characteristics of graph neural network learning and reasoning network relational data and the captured global information of the network, on the basis of the current network performance prediction methods, an intelligent prediction method of network performance based on graph neural network was proposed.Aiming at the complex network information, through the research of network system abstraction and network performance modeling, the network information can be transformed into the graph space convolution was used to process the message passing process of graph network nodes to realize the relationship reasoning between network information.The graph neural network model for network performance prediction was studied, and a graph neural network architecture which could deal with traffic matrix, network topology, routing strategy and node configuration was proposed.Finally, the experiments show that the model can better achieve accurate prediction of the network performance including delay, jitter and packet loss rate.

Key words: graph neural network, network performance prediction, network modeling, network analysis

CLC Number:

TP393

Yijiang LI, Huibiao YE, Renhua XIE, Jiali LOU, Danna ZHUANG, Chuanhuang LI. Intelligent prediction method of network performance based on graph neural network[J]. Telecommunications Science, 2022, 38(3): 143-157.

Figures/Tables 25

References 28

[1]	2020年中国互联网发展趋势报告[EB]. 2020.
	Report on China's Internet Development Trend in 2020[EB]. 2020.
[2]	LIU L K , ZHANG H L , SHI J T ,et al. I2P anonymous communication network measurement and analysis[C]// Smart Computing and Communication,4th International Conference. Switzerland:Springer Cham, 2019: 105-115.
[3]	CHEN Y , HU J Y , ZHAO H ,et al. Measurement and analysis of the swarm social network with tens of millions of nodes[J]. IEEE Access, 2018:1.
[4]	戴冕, 程光, 周余阳 . 软件定义网络的测量方法研究[J]. 软件学报, 2019(6): 1853-1874.
	DAI M , CHENG G , ZHOU Y Y . Survey on measurement method in software-defined networking[J]. Journal of Software, 2019(6): 1853-1874.
[5]	JIA S Y , LUCKIE M , HUFFAKER B ,et al. Tracking the deployment of IPv6:topology,routing and performance[J]. Computer Networks, 2019(165): 106947.
[6]	马卫国, 郎为民, 张寅 ,等. 虚拟化网络功能测量问题研究[J]. 电信快报, 2020 (3): 1-5.
	MA W G , LANG W M , ZHANG Y ,et al. Research on function measurement of virtual network[J]. Telecommunications Information, 2020(3): 1-5.
[7]	辛建芳, 朱琦, 梁广俊 ,等. 基于排队论的 D2D 蜂窝异构网络的性能分析[J]. 信号处理, 2018,34(4): 391-399.
	XIN J F , ZHU Q , LIANG G J ,et al. Performance analysis based on queuing theory for D2D underlaying cellular networks[J]. Journal of Signal Processing, 2018,34(4): 391-399.
[8]	熊兵, 左明科, 黎维 ,等. 面向软件定义核心网的 OpenFlow分组转发优先制排队模型研究[J]. 电子学报, 2019,47(10): 2040-2049.
	XIONG B , ZUO M K , LI W ,et al. A prioritized queueing model of OpenFlow packet forwarding in software-defined core networks[J]. Acta Electronica Sinica, 2019,47(10): 2040-2049.
[9]	李庆华, 陈志刚, 张连明 ,等. 基于网络演算的无线自组网QoS性能确定上界研究[J]. 通信学报, 2008(29): 32-39.
	LI Q H , CHEN Z G , ZHANG L M ,et al. Deterministic upper bounds on QoS performance about wireless ad hoc network based on network calculus[J]. Journal on Communications, 2008(29): 32-39.
[10]	任双印 . 基于网络演算的软件定义网络服务质量研究[D]. 长沙：国防科技大学, 2018.
	REN S Y . A software defined network QoS research based on network calculus[D]. Chang sha:National University of Defense Technology, 2018.
[11]	GEYER F , BONDORF S . DeepTMA:predicting effective contention models for network calculus using graph neural networks[C]// IEEE INFOCOM 2019-IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2019.
[12]	XIAO S H , HE D D , GONG Z B . Deep-Q:traffic-driven QoS inference using deep generative network[C]// The 2018 Workshop on Network Meets AI ＆ ML. New York:ACM Press, 2018: 67-73.
[13]	GEYER F , . DeepComNet:Performance evaluation of network topologies using graph-based deep learning[C]// Proceedings of the 11th EAI International Conference on Performance Evaluation Methodologies and Tools. New York:ACM Press, 2017.
[14]	NAKASHIMA M , SIM A , KIM J . Evaluation of deep learning models for network performance prediction for scientific facilities[C]// HPDC'20:The 29th International Symposium on High-Performance Parallel and Distributed Computing. New York:ACM Press, 2020.
[15]	SHI ZP , LI J , WU CT . DeepDDoS:online DDoS attack detection[C]// 2019 IEEE Global Communications Conference (GLOBECOM). Piscataway:IEEE Press, 2020.
[16]	GORI M , MONFARDINI G , SCARSELLI F . A new model for learning in graph domains[C]// IEEE International Joint Conference on Neural Networks. Piscataway:IEEE Press, 2005.
[17]	SCARSELLI F , GORI M , TSOI A C ,et al. The graph neural network model[J]. IEEE Transactions on Neural Networks, 2009,20(1): 61-80.
[18]	XIAN XF , FANG LG , SUN SM . ReGNN:A repeat aware graph neural network for session-based recommendations[J]. IEEE Access, 2020,8: 98518-98525.
[19]	RAGESH R , SELLAMANICKAM S , LINGAM V ,et al. A graph convolutional network composition framework for semi-supervised classification[EB]. 2020.
[20]	WANG ZY , GOMBOLAY M . Learning scheduling policies for multi-robot coordination with graph attention networks[J]. IEEE Robotics and Automation Letters, 2020,5(3): 4509-4516.
[21]	FAN S,HUANG B,Labeled graph generative adversarial networks[EB]. 2019.
[22]	GUO SN , LIN YF , FENG N ,et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[C]// Proceedings of the AAAI Conference on Artificial Intelligence,Piscataway:IEEE Press, 2019(33): 922-929.
[23]	PENG H , WANG HF , DU BW ,et al. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting[J]. Information Sciences, 2020(521): 277-290.
[24]	GILMER J , SCHOENHOLZ S S , RILEY P F ,et al. Neural message passing for quantum chemistry[C]// Proceedings of the 34th International Conference on Machine Learning. Cambridge:JMLR, 2017(70): 1263-1272.
[25]	RUSEK K , JOSé SUáREZ-VARELA , MESTRES A ,et al. Unveiling the potential of graph neural networks for network modeling and optimization in SDN[C]// Proceedings of the 2019 ACM Symposium on SDN Research. New York:ACM Press, 2019: 140-151.
[26]	BENGIO S , VINYALS O , JAITLY N ,et al. Scheduled sampling for sequence prediction with recurrent neural networks[C]// Advances in Neural Information Processing Systems.New York:Curran Associates,Inc. , 2015: 1171-1179.
[27]	HUANG XH , ZENG M , XIE K . Intelligent traffic control for QoS optimization in hybrid SDNs[J]. Computer Networks, 2021,189(2): 107877.
[28]	CLARK D D , PARTRIDGE C , RAMMING J C ,et al. A knowledge plane for the internet[C]// Conference on Applications. New York:ACM Press, 2003: 3-10.

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节点参数	描述
Level-QoS	支持的QoS类别数
Queue-Size	节点的每个输出队列的大小
Schedule-Policy	服务QoS队列的策略
Schedule-Weight	与每个 QoS 队列关联的权重列表（仅WFQ、DRR）

数据集	类别	总计
NSFNet	训练集	208 000
	验证集	52 000
	测试集	112 000

超参数	模型名称
超参数	GNN0	GNN1	GNN2	GNN3
GRU层数	2	2	2	2
GRU神经元数	16	32	32	32
MLP全连接层数	2	2	4	4
MLP各层神经元数	256	256	128	256
激活函数	ReLU	ReLU	ReLU	ReLU

评价参数		模型名称
评价参数		GNN0	GNN1	GNN2	GNN3
RMSE	系数时延	0.044	0.028	0.049	0.031
	抖动	0.033	0.027	0.035	0.031
	丢包率	0.042	0.029	0.047	0.033
Pearson系数	系数时延	0.997	0.9975	0.995	0.995
	抖动	0.975	0.985	0.975	0.978
	丢包率	0.997	0.998	0.995	0.996

Intelligent prediction method of network performance based on graph neural network

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