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

Jing Bi, Haitao Yuan, Kangyuan Xu, Haisen Ma, Meng Chu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

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 net-work 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.

Original languageEnglish (US)
Title of host publication2022 IEEE International Conference on Robotics and Automation, ICRA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3865-3870
Number of pages6
ISBN (Electronic)9781728196817
DOIs
StatePublished - 2022
Event39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States
Duration: May 23 2022May 27 2022

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference39th IEEE International Conference on Robotics and Automation, ICRA 2022
Country/TerritoryUnited States
CityPhiladelphia
Period5/23/225/27/22

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Control and Systems Engineering

Keywords

  • Cloud computing
  • LSTM
  • Savitzky-Golay filter
  • TCNs
  • Time series prediction

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