Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 88-95.doi: 10.6040/j.issn.1672-3961.0.2018.342

• Machine Learning & Data Mining • Previous Articles     Next Articles

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

Fang GUO1(),Lei CHEN1,2,3,Ziwen YANG1   

  1. 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)

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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

CLC Number: 

  • TP393

Fig.1

The flows and operations of LSTM"

Fig.2

The flows and operations of GRU"

Fig.3

The flows and operations of MGU"

Fig.4

Slide window for building training data"

Fig.5

Network traffic prediction model based on MGU"

Fig.6

Traffic prediction results about the 53th OD flowin different models"

Table 1

The total parameters and precision ofdifferent models"

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

Fig.7

The distribution of MAE in MGU model and FFNNmodel for predicting network traffic"

Fig.8

The distribution of MAE in MGU model and LSTM model for predicting network traffic"

Fig.9

The distribution of MAE in MGU model andFFNN model for predicting network traffic"

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