Prediction of Network Traffic Load on High Variability Data Based on Distance Correlation

Lo Pang Yun Ting, Tiago Koketsu Rodrigues, Nei Kato, Kun Ta Chuang

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

Abstract

Accurate network traffic load (TL) prediction is essential in many networking applications. However, the real TLs in practical networks may have high variability and are difficult to be predicted, which may severely affect users' quality of experience (QoE). To address this problem, we first analyze the real-world network traffic dataset to investigate real TLs properties and find out the distance-correlation between regions in a spatial graph have the potential to improve the prediction result. Hence, we propose a time-series model based method to consider the distance-correlation in an efficient way. Empirically, experimental studies on real data demonstrate that our proposed method can effectively reduce at least 10% error value on regions with high-variability TLs. Finally, we further discuss the impact of the distance-correlation on the TL prediction.

Original languageEnglish
Title of host publication2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194844
DOIs
Publication statusPublished - 2020 Nov
Event92nd IEEE Vehicular Technology Conference, VTC 2020-Fall - Virtual, Victoria, Canada
Duration: 2020 Nov 18 → …

Publication series

NameIEEE Vehicular Technology Conference
Volume2020-November
ISSN (Print)1550-2252

Conference

Conference92nd IEEE Vehicular Technology Conference, VTC 2020-Fall
Country/TerritoryCanada
CityVirtual, Victoria
Period20/11/18 → …

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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