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- Zhu Tianhao
- Dept. of Electrical Engineering and Information System, University of Tokyo
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- Lee Jiwon
- Dept. of Electrical Engineering and Information System, University of Tokyo
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- Du Bojian
- Dept. of Electrical Engineering and Information System, University of Tokyo
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- Kondo Ryoma
- Dept. of Electrical Engineering and Information System, University of Tokyo
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- Matsuura Kentaro
- Dept. of Electrical Engineering and Information System, University of Tokyo
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- Morikawa Hiroyuki
- Dept. of Electrical Engineering and Information System, University of Tokyo
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- Narusue Yoshiaki
- Dept. of Electrical Engineering and Information System, University of Tokyo
説明
<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>
収録刊行物
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- IEICE Communications Express
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IEICE Communications Express 13 (5), 159-162, 2024-05-01
一般社団法人 電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1390018518955973888
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- ISSN
- 21870136
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
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- 抄録ライセンスフラグ
- 使用不可