Large-scale Network Traffic Prediction With LSTM and Temporal Convolutional Networks

被引:12
作者
Bi, Jing [1 ]
Yuan, Haitao [2 ]
Xu, Kangyuan [1 ]
Ma, Haisen [1 ]
Zhou, Mengchu [3 ]
机构
[1] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
[2] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
[3] New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA
来源
2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2022) | 2022年
基金
中国国家自然科学基金;
关键词
Time series prediction; Savitzky-Golay filter; LSTM; TCNs; Cloud computing; NEURAL-NETWORKS; DROPOUT;
D O I
10.1109/ICRA46639.2022.9812427
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Real-time and precise prediction for traffic of networks is critically important for allocating the optimal computing/network resources based on users' business requirements, analyzing the network performance, and realizing intelligent congestion control and high-accuracy anomaly detection. The dramatic growth of users' applications significantly increases the volume, uncertainty, and complexity of workload, thereby making it highly challenging to precisely predict future network traffic. Temporal Convolutional Networks (TCNs) and Long Short-Term Memory (LSTM) can be effectively used to analyze and predict time series. This work designs an improved prediction approach for the prediction of network traffic, which combines a Savitzky-Golay filter, TCN, and LSTM, called ST-LSTM for short. It first removes the noise of data with the filter of Savitzky-Golay. It then investigates temporal characteristics of data by using TCN. At last, it investigates the long-term dependency in the time series by using LSTM. Experimental results on a real-life website dataset show the prediction accuracy of ST-LSTM is higher than autoregressive integrated moving average, support vector regression, eXtreme Gradient Boosting, backpropagation, TCN, and LSTM, in terms of several commonly used performance indicators.
引用
收藏
页码:3865 / 3870
页数:6
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