Evaluation on eBPF-based network failure prediction using AutoGluon

DOI PDF 参考文献7件
  • Zhu Tianhao
    Dept. of Electrical Engineering and Information System, University of Tokyo
  • Lee Jiwon
    Dept. of Electrical Engineering and Information System, University of Tokyo
  • Du Bojian
    Dept. of Electrical Engineering and Information System, University of Tokyo
  • Kondo Ryoma
    Dept. of Electrical Engineering and Information System, University of Tokyo
  • Matsuura Kentaro
    Dept. of Electrical Engineering and Information System, University of Tokyo
  • Morikawa Hiroyuki
    Dept. of Electrical Engineering and Information System, University of Tokyo
  • Narusue Yoshiaki
    Dept. of Electrical Engineering and Information System, University of Tokyo

抄録

<p>This study evaluates an extended Berkeley Packet Filter (eBPF)-based network failure prediction method using Autogluon-Tabular to process the fine-grained network information extracted by eBPF. The extracted information is considered as input features of the proposed model, which aims to predict the subsequent packet loss and determine a network failure event before it causes a huge impact. Supervised learning and semi-supervised learning are both adopted in Autogluon. The accuracy and detection time are evaluated as the main criteria. Simulation results show that F1 scores exceed 0.9 for our proposed method, and the proposed method can achieve prediction for potential failure events within 30 and 40 seconds when symptoms such as packet loss occur.</p>

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