| 研究課題/領域番号 |
23K16875
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分60060:情報ネットワーク関連
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| 研究機関 | 京都情報大学院大学 |
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研究代表者 |
望月 バドル 京都情報大学院大学, その他の研究科, 准教授 (10838460)
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| 研究期間 (年度) |
2023-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
3,770千円 (直接経費: 2,900千円、間接経費: 870千円)
2025年度: 780千円 (直接経費: 600千円、間接経費: 180千円)
2024年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2023年度: 1,690千円 (直接経費: 1,300千円、間接経費: 390千円)
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| キーワード | Inband Network Telemetry / P4 / SDN / Network Failure / Data-plane / Failure Detection / Failure Localization |
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| 研究開始時の研究の概要 |
Theme 1: Development of module for lightweight INT metadata collection using machine learning
Theme 2:
- Development of fast failure detection module
- Development of precise failure localization module
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| 研究実績の概要 |
The proposed research is dedicated to network failure detection and localization in optical core networks. The aim is to leverage the low latency of data plane programmable network devices and In-band Network Telemetry (INT) for both detection and precise localization in real-time of network failures. This research is to be performed in three years under three phases:
1: Precise localization of network failures
2: Real-time detection of network failures
3: Dynamic orchestration of INT metadata collection
In this first year, Phase 1 has been achieved and research work on phase 2 has started. In the first year, a precise failure localization algorithm has been developed. It uses backward probing in order to determine the exact switch and link where the network failure has occurred.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The research is having a smooth progress: one journal paper on precise failure localization algorithm is under submission and the development of phase 2 has started.
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| 今後の研究の推進方策 |
We will continue research work on phase 2 and phase 3 as follows.
Phase 2: Real-time detection of network failures. Our main objectives will be to: 1) Set up path tables and 2) Detect failure through timed-out path table entries
Phase 3: Dynamic orchestration of the collection of INT metadata. Our main objectives will be to: 1) Maximize the number of collected INT metadata and 2) Identify which INT metadata is important to be collected
After phase 3 is finished, we will integrate the modules of all three phases into a global framework and investigate its performance.
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