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

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728194844
DOI
出版ステータスPublished - 2020 11月
イベント92nd IEEE Vehicular Technology Conference, VTC 2020-Fall - Virtual, Victoria, Canada
継続期間: 2020 11月 18 → …

出版物シリーズ

名前IEEE Vehicular Technology Conference
2020-November
ISSN(印刷版)1550-2252

Conference

Conference92nd IEEE Vehicular Technology Conference, VTC 2020-Fall
国/地域Canada
CityVirtual, Victoria
Period20/11/18 → …

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 電子工学および電気工学
  • 応用数学

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