DeepDefrag: Spatio-Temporal Defragmentation of Time-Varying Virtual Networks in Computing Power Network based on Model-Assisted Reinforcement Learning

Huangxu Ma; Jiawei Zhang; Zhiqun Gu; Hao Yu; Tarik Taleb; Yuefeng Ji

European Conference on Optical Communication (ECOC) 2022
Technical Digest Series (Optica Publishing Group, 2022),
paper Tu5.59

DeepDefrag: Spatio-Temporal Defragmentation of Time-Varying Virtual Networks in Computing Power Network based on Model-Assisted Reinforcement Learning

Huangxu Ma, Jiawei Zhang, Zhiqun Gu, Hao Yu, Tarik Taleb, and Yuefeng Ji

European Conference and Exhibition on Optical Communication 2022

Basel Switzerland
18–22 September 2022
ISBN: 978-1-957171-15-9

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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," in European Conference on Optical Communication (ECOC) 2022, J. Leuthold, C. Harder, B. Offrein, and H. Limberger, eds., Technical Digest Series (Optica Publishing Group, 2022), paper Tu5.59.

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Abstract

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.

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