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DeepDefrag : spatio-temporal defragmentation of time-varying virtual networks in computing power network based on model-assisted reinforcement learning |
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| Author: | Ma, Huangxu1,2; Zhang, Jiawei1,2; Gu, Zhiqun1,2; |
| Organizations: |
1State Key Lab of Information Photonics and Optical Communications, Beijing 2University of Posts and Telecommunications (BUPT), Beijing, China 3Center for Wireless Communications, University of Oulu, Oulu, Finland |
| Format: | article |
| Version: | accepted version |
| Access: | open |
| Online Access: | PDF Full Text (PDF, 0.7 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe202301276209 |
| Language: | English |
| Published: |
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2022
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| Publish Date: | 2023-01-27 |
| Description: |
AbstractWe propose DeepDefrag, a model-assisted reinforcement learning for spatio-temporal defragmentation of time-varying virtual networks in a cross-layer optical network testbed, which realizes the efficient utilization of computing nodes and lightpaths by co-optimizing scheduling and embedding with fragment matching, reduces >13.5% cost of computing power network. see all
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| ISBN: | 978-1-957171-15-9 |
| ISBN Print: | 978-1-6654-7557-0 |
| Article number: | Tu5.59 |
| Host publication: |
2022 European Conference on Optical Communication (ECOC), 18-22 Sept. 2022 |
| Conference: |
European Conference on Optical Communication |
| Type of Publication: |
A4 Article in conference proceedings |
| Field of Science: |
213 Electronic, automation and communications engineering, electronics |
| Subjects: | |
| Funding: |
This work is supported by the National Nature Science Foundation of China Projects (61871051, 61971055), the BUPT Innovation and Entrepreneurship Support Programs (2022-YC-T006, 2022-YC-A004). |
| Copyright information: |
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