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

摘要

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.

原文English
主出版物標題2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728194844
DOIs
出版狀態Published - 2020 十一月
事件92nd IEEE Vehicular Technology Conference, VTC 2020-Fall - Virtual, Victoria, Canada
持續時間: 2020 十一月 18 → …

出版系列

名字IEEE Vehicular Technology Conference
2020-November
ISSN(列印)1550-2252

Conference

Conference92nd IEEE Vehicular Technology Conference, VTC 2020-Fall
國家/地區Canada
城市Virtual, Victoria
期間20-11-18 → …

All Science Journal Classification (ASJC) codes

  • 電腦科學應用
  • 電氣與電子工程
  • 應用數學

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