FlowDiviner: Spatio-Temporal Network TrafficPrediction Method Based on Graph Neural Network
Liang Qin, Wenting Wei, Yinghao Ma
Xidian UniversityXi’an, Shaanxi, China
ACM Reference Format:
Liang Qin, Wenting Wei, Yinghao Ma. 2021. FlowDiviner: SpatioTemporal Network Traffic Prediction Method Based on Graph Neural Network. In 5th Asia-Pacific Workshop on Networking (APNet2021) (APNet ’21), June 24–25, 2021, Shenzhen, China, China. ACM,New York, NY, USA, 2 pages. https://doi.org/10.1145/3469393.3469671

1INTRODUCTION

End-to-end traffic prediction plays an important role in network management. However, network traffic volume is hard
to forecast due to its characteristics of non-linear, abrupt,and time-sensitive.
The existing works mainly contain linear and non-linear
traffic prediction methods.The liner traffic predictors represented by ARIMA are mainly effective for short term traffic
but not work well for long term traffic. Thanks to its powerful non-linear representation ability, deep learning[6] isused by researchers to predict the future network traffic
successfully[3][2]. However, most of existing works onlyconsider the temporal correlation of traffic and ignore thespatial correlation of network traffic, resulting in poor accuracy. The main reason is that network traffic itself has
strong spatial correlation as well as temporal correlation.
The burst traffic of a node in the network will have an impact on the traffic trend of the surrounding nodes in addition
to the traffic trend of this node at the next moment.
We proposed a new long term network traffic prediction
method FlowDiviner. FlowDiviner is an encoder and decoderarchitecture. The GCN model is used to capture the spatial
characteristics of traffic from the network topology. The
Multi-Head Attention layer is used to extract the dynamic
trend of network traffic to obtain temporal characteristics. AtPermission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies are notmade or distributed for profit or commercial advantage and that copies bear
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https://doi.org/10.1145/3469393.3469671

the same time, there’s a Middle Attention Block between theencoder and the decoder, determine the relationship between
the historical and the future state of traffic by using themechanism of Multi-Head Attention, therefore, reduce theinfluence of the cumulative error of the model by the way.
Preliminary experiments on real Abilene network traffic
dataset show that FlowDiviner has outstanding performancein long term traffic prediction.

2

TRAFFIC PREDICTION MODEL

Figure 1: overview framework of FlowDiviner.
Spatial correlation extraction: Graph convolutional neural network[1] can effectively process non-Euclidean datasuch as graph. Since the network topology can be modeled
as a graph, we use the graph convolutional neural network
to extract the spatial characteristics of traffic data. A GCN
model contains multiple convolutional layers, and its feature
propagation rule can be expressed as:H (l +1) = σ (D̃ − 2 ÃD̃ − 2 H (l )W (l ) )11(1)where, Ã=A + I N is the adjacency matrix that adds selfconnection, A ∈ R N ×N is the adjacency matrix, I N is theidentity matrix, W (l ) is the weight matrix of layer l, H (l ) isthe output of layer l, and H (0) = X , X ∈ R N ×P is the flow
characteristic matrix, σ (•) is the activation function. Here,we choose a GCN model that contains two layers of convolution layer for processing graph data, which can be expressedas:⌢⌢Z = f (X , A) = σ (AReLU (AXW (0) )W (1) )(2)where,Z = f (X , A) ∈ R N ×K⌢

1

1

is the output feature of K step lengths predicted. A = D̃ − 2 ÃD̃ − 2is the pre-processing step, D̃ is the node degree matrix,
W (0) ∈ R P ×H is the weight of input layer to hidden layer,

APNet ’21, June 24–25, 2021, Shenzhen, China, China

Liang Qin, Wenting Wei, Yinghao Ma.

W (1) ∈ R H ×K is the weight of hidden layer to output layer, σand ReLU are activation functions.
Temporal correlation extraction: We use the multihead attention mechanism[5] to model the relationship between current and past time step traffic states.
cc
α t,t= so f t max(et,t)jj(3)cwhere, α t,tis the attention score in the c t h head, indicatingjc
the importance of time step t to t j . et,t
represents the corj
relation between time step t and t j in the c t h head, and thecalculation method is as follows:
DEA • Wq hvl i ,t ,Wk hvl i ,t jc(4)
et,t=
√
jdc

where, hvi ,t and hvi ,t j are respectively the hidden statesof nodes in time step t to t j , Wq and Wk are the trainable
parameter matrix, A is the adjacency matrix, and dc is thescaling factor of the c t h head.
After getting the attention score, the node vi hidden statein the time step t j can be updated as:ÕCcl
α t,t
•W
•h(5)
hvl i ,t j =∥c=1vv i ,tjt ∈N t j

where, ∥ represents concatenation operation, Wv represents
value matrix.
Middle Attention Block: The cumulative error increases
with the increase of the predicted time step. We added a Middle Attention Block between the encoder and the decoder tomodel the past and future time steps of the traffic state. Themodule includes a multi-head attention layer, which uses theencoded traffic characteristics as input to the decoder to further extend the ability of the model to focus on a certain state.
For any node in the network, the correlation between future
time step tk and historical time step tp can be expressed as:
ccβt,t= so f t max(et,t)(6)jj
D
EA • Wqc hvl i ,t ,Wkc hvl i ,t jcet,t j =(7)√dcAfter the Attention score is calculated, the correlation between the traffic status of the time step P after historicalcoding can be obtained, as shown in Equation8 :n ÕPoCchvl i ,t j =∥c=1βt,t• Wvc • hvl i ,t(8)jt =13PRELIMINARY EVALUATION

We train and evaluate FlowDiviner based on a real traffic
dataset collected from Abilene Network[4]. The Abilene network consists of 12 nodes connected by 13 links. At the sametime, in Abilene traffic data set, each Timestep within definedas 5 minutes, to smooth the data distribution and speed up

the model convergence, we use maximum minimum normalization methods for processing the raw data.
Root mean square error (RMSE), mean absolute error(MAE) and mean absolute percentage error (MAPE) are used
to evaluate the performance of FlowDiviner. The better theperformance, the closer the values of these three indexes areto 0. We choose ARIMA, LSTM and GRU as Baseline methods
to compare with FlowDiviner in terms of performance.
Table 1: Evaluation Result
Time Metrics ARIMA LSTM GRU FlowDiviner
RMSE 415.36 324.67 316.48
238.47
15min MAE 315.86 164.55 158.99
125.41
MAPE 7.43% 5.68% 5.19%
5.48%
RMSE 653.95 449.19 439.48
407.1630min MAE 346.52 199.37 199.26
184.34MAPE 9.18% 6.89% 6.72%6.24%
RMSE 885.95 565.58 547.02
486.5860min MAE 420.67 266.75 249.87
224.57MAPE 13.92% 8.97% 8.75%
7.48%
Table1 shows the results of each index in 15 minutes (3steps), 30 minutes (6 steps) and 60 minutes (12 steps) prediction on the traffic data set generated by the Abilene networkwith different schemes.we can find that FlowDiviner has
achieved better performance in all time domains, especiallyin the long-term prediction, with the maximum improvementof about 15%, which proves the necessity of considering spatial correlation in the flow prediction task.

4CONCLUSION

In this poster, we propose a new network traffic prediction
method based on Graph neural network . Preliminary experiments show that FlowDiviner is effective. In the future, wewill further verify the performance of FlowDiviner in larger
scale networks, such as Geant23, Gemany50, etc.

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