Presentation | 2022-03-04 A Study on Network Fault Recovery Framework with Reinforcement Learning Tatsuji Miyamoto, Genichi Mori, Yusuke Suzuki, Tomohiro Otani, Junji Takemasa, Yuki Koizumi, Toru Hasegawa, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Network operator automates failure recovery operation with implementing programs for each service and failure case. However, 5G introduces additional complexity into network monitoring and management due to the network function being independent of the hardware. In fact, a vast amount of workflows for automated the failure recovery operation have to be created and maintained per type of service and failure case. To address the above problems, this paper proposes a reinforcement learning-based fault recovery framework by applying the reinforcement learning algorithm. We demonstrate the effectiveness of the proposed framework for network failure recovery operation with simulation results. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Network management / Failure recovery / Automation / Machine learning |
Paper # | ICM2021-54 |
Date of Issue | 2022-02-24 (ICM) |
Conference Information | |
Committee | ICM |
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Conference Date | 2022/3/3(2days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | Online |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | |
Chair | Kazuhiko Kinoshita(Tokushima Univ.) |
Vice Chair | Haruo Ooishi(NTT) / Eiji Takahashi(NEC) |
Secretary | Haruo Ooishi(Bosco) / Eiji Takahashi(Fujitsu) |
Assistant | Yoshifumi Kato(NTT) |
Paper Information | |
Registration To | Technical Committee on Information and Communication Management |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A Study on Network Fault Recovery Framework with Reinforcement Learning |
Sub Title (in English) | |
Keyword(1) | Network management |
Keyword(2) | Failure recovery |
Keyword(3) | Automation |
Keyword(4) | Machine learning |
1st Author's Name | Tatsuji Miyamoto |
1st Author's Affiliation | KDDI Research, Inc./Osaka University(KDDI Research, Inc./Osaka Univ.) |
2nd Author's Name | Genichi Mori |
2nd Author's Affiliation | KDDI Research, Inc.(KDDI Research, Inc.) |
3rd Author's Name | Yusuke Suzuki |
3rd Author's Affiliation | KDDI Corporation(KDDI) |
4th Author's Name | Tomohiro Otani |
4th Author's Affiliation | KDDI Research, Inc.(KDDI Research, Inc.) |
5th Author's Name | Junji Takemasa |
5th Author's Affiliation | Osaka University(Osaka Univ.) |
6th Author's Name | Yuki Koizumi |
6th Author's Affiliation | Osaka University(Osaka Univ.) |
7th Author's Name | Toru Hasegawa |
7th Author's Affiliation | Osaka University(Osaka Univ.) |
Date | 2022-03-04 |
Paper # | ICM2021-54 |
Volume (vol) | vol.121 |
Number (no) | ICM-399 |
Page | pp.pp.63-67(ICM), |
#Pages | 5 |
Date of Issue | 2022-02-24 (ICM) |