University of Oulu

H. Ma, J. Zhang, Z. Gu, H. Yu, T. Taleb and Y. Ji, "DeepDefrag: Spatio-Temporal Defragmentation of Time-Varying Virtual Networks in Computing Power Network based on Model-Assisted Reinforcement Learning," 2022 European Conference on Optical Communication (ECOC), Basel, Switzerland, 2022, pp. 1-4.

DeepDefrag : spatio-temporal defragmentation of time-varying virtual networks in computing power network based on model-assisted reinforcement learning

Saved in:
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:
Language: English
Published: , 2022
Publish Date: 2023-01-27


We 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

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
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: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.