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アイテム
Capacitated Shortest Path Tour-Based Service Chaining Adaptive to Changes of Service Demand and Network Topology
http://hdl.handle.net/10061/0002000667
http://hdl.handle.net/10061/000200066700f15e86-01f7-4010-a106-83245424fcd3
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-11-14 | |||||||
| タイトル | ||||||||
| タイトル | Capacitated Shortest Path Tour-Based Service Chaining Adaptive to Changes of Service Demand and Network Topology | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Network functions virtualization (NFV) | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | software defined networking (SDN) | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | service chaining (SC) | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | capacitated shortest path tour problem (CSPTP) | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | deep reinforcement learning (DRL) | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | graph neural network (GNN) | |||||||
| 資源タイプ | ||||||||
| 資源タイプ | journal article | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| 著者 |
原, 崇徳
× 原, 崇徳× Sasabe, Masahiro
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | To achieve sustainable networking, network service providers have expressed significant interest in employing automated network operations that integrate network functions virtualization (NFV), software-defined networking (SDN), and machine learning (ML). In the context of NFV/SDN, a certain network service is regarded as a sequence of virtual network functions (VNFs) forming a service chain. The service chaining (SC) problem aims at establishing an appropriate service path from an origin node to a destination node where the VNFs are executed at intermediate nodes in the required order under resource constraints on nodes and links. SDN enables programmable configurations on forwarding devices (i.e., switches and routers) for traffic forwarding between VNFs. In our previous work, we formulated the SC problem as an integer linear program (ILP) based on the capacitated shortest path tour problem (CSPTP), which is an extended version of SPTP with additional node and link capacity constraints. Furthermore, we developed Lagrangian heuristics to solve the problem by considering the balance between optimality and computational complexity. In this paper, we propose a deep reinforcement learning (DRL) framework coupled with the graph neural network (GNN) to realize CSPTP-based SC that adapts to changes of service demand and/or network topology. Numerical results show that the proposed framework achieves nearly optimal SC with higher learning speed compared to the conventional deep Q-Network based approach. Moreover, it performs well when confronted with variations in service demand and exhibits competitive performance compared to the ILP solutions across the majority of 243 real-world topologies. | |||||||
| 書誌情報 |
en : IEEE Transactions on Network and Service Management 巻 21, 号 2, p. 1344-1358, 発行日 2024-01-09 |
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| 出版者 | ||||||||
| 出版者 | IEEE | |||||||
| ISSN | ||||||||
| 収録物識別子タイプ | EISSN | |||||||
| 収録物識別子 | 1932-4537 | |||||||
| 出版者版DOI | ||||||||
| 関連タイプ | isReplacedBy | |||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | https://doi.org/10.1109/TNSM.2024.3351737 | |||||||
| 出版者版URI | ||||||||
| 関連タイプ | isReplacedBy | |||||||
| 識別子タイプ | URI | |||||||
| 関連識別子 | https://ieeexplore.ieee.org/abstract/document/10384810 | |||||||
| 権利 | ||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||
| 権利情報 | c 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | |||||||
| 著者版フラグ | ||||||||
| 出版タイプ | NA | |||||||
