Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Deep Reinforcement Learning Based DNN Model Partition in Edge Computing-enabled Metro Optical Network

Not Accessible

Your library or personal account may give you access

Abstract

A deep reinforcement learning based DNN model partition and deployment algorithm is proposed between edge nodes and cloud in metro optical network. Simulation results show that the algorithm can deploy more DNN tasks than heuristics.

© 2021 The Author(s)

PDF Article
More Like This
Adaptive DNN Model Partition and Deployment in Edge Computing-enabled Metro Optical Interconnection Network

Mingzhe Liu, Yajie Li, Yongli Zhao, Hui Yang, and Jie Zhang
Th2A.28 Optical Fiber Communication Conference (OFC) 2020

A Waveband Routing Method in Optical Networks Based on the Deep Reinforcement Learning

Yang Liu, Bin Chen, Gongchao Su, Mingjun Dai, and Xiaohui Lin
JS3C.1 Optoelectronics and Communications Conference (OECC) 2021

A Subcarrier-Slot Autonomous Partition Scheme Based on Deep-Reinforcement-Learning in Elastic Optical Networks

Xin Wang, Yue-Cai Huang, Jie Liu, and Siyuan Yu
M4A.211 Asia Communications and Photonics Conference (ACP) 2019

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.