Flexible Optical Network Enabled Hybrid Recovery for Edge Network with Reinforcement Learning

Lian, Meng; Gu, Rentao; Qu, Yongyao; Wang, Zihao; Ji, Yuefeng

doi:10.1364/ofc.2020.m1a.2

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…Network recovery procedures are then executed, and the network can be reconfigured by a physical layer controller. Aiming at the faulty nodes with huge traffic in the edge network, Lian et al (2020) proposed a fault recovery algorithm. By running the dual migration and reinforcement learning-based recovery scheme, the time for failure recovery is proved to be independent of the number of failed services.…”

Section: Related Workmentioning

confidence: 99%

A 3-CS Distributed Federated Transfer Learning Framework for Intelligent Edge Optical Networks

et al. 2021

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With the rapid development of optical network and edge computing, the operation efficiency of the edge optical network has become more and more important, requiring an intelligent approach to enhance the network performance. To enhance the intelligence of the edge optical network, this article firstly provides the demand for the development of edge optical networks. Then, a cross-scene, cross-spectrum, and cross-service (3-CS) architecture for edge optical networks is presented. Finally, a federated transfer learning (FTL) framework, realizing a distributed intelligence edge optical network, is proposed. The usability of the proposed framework is verified by simulation.

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Section: Related Workmentioning

confidence: 99%

A 3-CS Distributed Federated Transfer Learning Framework for Intelligent Edge Optical Networks

et al. 2021

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“…Following iterative approach, the method excludes lightpaths with poor QoT as estimated by the ML classifier, until either a feasible solution for all lightpaths is found, or the upper limit of iteration count is reached. In [19], to achieve a fast network recovery from an IP node failure in IP-over-EON, a Q-learning based recovery algorithm is presented. Kiran et al propose algorithms based on Q-learning to solve path selection and wavelength selection in optical burst switch (OBS) networks with objective to minimize the burst loss probability [20].…”

Section: Introductionmentioning

confidence: 99%

Q-Learning Based Energy-Efficient Network Planning in IP-over-EON

Biswas¹,

Akhtar²,

Adhya³

et al. 2021

Preprint

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During network planning phase, optimal network planning implemented through efficient resource allocation and static traffic demand provisioning in IP-over-elastic optical network (IP-over-EON) is significantly challenging compared with the fixed-grid wavelength division multiplexing (WDM) network due to increased flexibility in IP-over-EON. Mathematical optimization models used for this purpose may not provide solution for large networks due to large computational complexity. In this regard, a greedy heuristic may be used that intuitively selects traffic elements in sequence from static traffic demand matrix and attempts to find the best solution. However, in general, such greedy heuristics offer suboptimal solutions, since appropriate traffic sequence offering the optimal performance is rarely selected. In this regard, we propose a reinforcement learning technique (in particular a Q-learning method), combined with an auxiliary graph (AG)-based energy efficient greedy method to be used for large network planning. The Q-learning method is used to decide the suitable sequence of traffic allocation such that the overall power consumption in the network reduces. In the proposed heuristic, each traffic from the given static traffic demand matrix is successively selected using Q-learning technique and provisioned using the AG-based greedy method.

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Artificial intelligence-driven autonomous optical networks: 3S architecture and key technologies

Yang

et al. 2020

Sci. China Inf. Sci.

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Flexible Optical Network Enabled Hybrid Recovery for Edge Network with Reinforcement Learning

Abstract: The proposed hybrid recovery utilizes flexible optical network with reinforcement learning to recover IP fault for edge network. The testbed experiments indicate, the recovery time is 20% of rerouting-based strategy for a heavy-loaded network.

Cited by 6 publications

References 2 publications

A 3-CS Distributed Federated Transfer Learning Framework for Intelligent Edge Optical Networks

A 3-CS Distributed Federated Transfer Learning Framework for Intelligent Edge Optical Networks

Q-Learning Based Energy-Efficient Network Planning in IP-over-EON

Artificial intelligence-driven autonomous optical networks: 3S architecture and key technologies

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