| Presentation | 2019-09-06 Dynamic Virtual Resource Allocation Method Using Multi-agent Deep Reinforcement Learning Akito Suzuki, Shigeaki Harada, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The network traffic demands have been changing dramatically in recent years due to the growth of various types of network service, e.g., high-quality video delivery and OS update. In order to maximize the utilization efficiency of limited network resources, network resource control technology is required to take a smooth and quick operation when the traffic demands changes. In this paper, we aim to develop the dynamic network resource control method using multi-agent deep reinforcement learning, which method can quickly optimize the network resources even when traffic demands changing drastically by learning the relationship between traffic demands pattern and optimal control in advance. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | NFV / Deep Reinforcement Learning / Network Control |
| Paper # | IN2019-29 |
| Date of Issue | 2019-08-29 (IN) |
| Conference Information | |
| Committee | NS / IN / CS |
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| Conference Date | 2019/9/5(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Research Institute of Electrical Communication, Tohoku Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Session management (SIP/IMS), Interoperability/Standardization, NGN/NwGN/Future networks, Cloud/Data center networks, SDN (OpenFlow, etc.)/NFV, IPv6, Machine learning, etc. |
| Chair | Yoshikatsu Okazaki(NTT) / Takuji Kishida(NTT-AT) / Hidenori Nakazato(Waseda Univ.) |
| Vice Chair | Akihiro Nakao(Univ. of Tokyo) / Kenji Ishida(Hiroshima City Univ.) / Jun Terada(NTT) |
| Secretary | Akihiro Nakao(Osaka Pref Univ.) / Kenji Ishida(NTT) / Jun Terada(NTT Communications) |
| Assistant | Shinya Kawano(NTT) / / Kazutaka Hara(NTT) / Hiroyuki Saito(OKI) |
| Paper Information | |
| Registration To | Technical Committee on Network Systems / Technical Committee on Information Networks / Technical Committee on Communication Systems |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Dynamic Virtual Resource Allocation Method Using Multi-agent Deep Reinforcement Learning |
| Sub Title (in English) | |
| Keyword(1) | NFV |
| Keyword(2) | Deep Reinforcement Learning |
| Keyword(3) | Network Control |
| 1st Author's Name | Akito Suzuki |
| 1st Author's Affiliation | NTT(NTT) |
| 2nd Author's Name | Shigeaki Harada |
| 2nd Author's Affiliation | NTT(NTT) |
| Date | 2019-09-06 |
| Paper # | IN2019-29 |
| Volume (vol) | vol.119 |
| Number (no) | IN-195 |
| Page | pp.pp.35-40(IN), |
| #Pages | 6 |
| Date of Issue | 2019-08-29 (IN) |