DeepDefrag : spatio-temporal defragmentation of time-varying virtual networks in computing power network based on model-assisted reinforcement learning
Ma, Huangxu; Zhang, Jiawei; Gu, Zhiqun; Yu, Hao; Taleb, Tarik; Ji, Yuefeng (2022-12-20)
Ma, Huangxu
Zhang, Jiawei
Gu, Zhiqun
Yu, Hao
Taleb, Tarik
Ji, Yuefeng
20.12.2022
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.
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© 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.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe202301276209
https://urn.fi/URN:NBN:fi-fe202301276209
Tiivistelmä
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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