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Suspect fault screen assisted graph aggregation network for intra-/inter-node failure localization in ROADM-based optical networks

Wang, Ruikun; Zhang, Jiawei; Yan, Shuangyi; Zeng, Chuidian; Yu, Hao; Gu, Zhiqun; Zhang, Bojun; Taleb, Tarik; Ji, Yuefeng (2023-05-26)

 
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URL:
https://doi.org/10.1364/JOCN.480970

Wang, Ruikun
Zhang, Jiawei
Yan, Shuangyi
Zeng, Chuidian
Yu, Hao
Gu, Zhiqun
Zhang, Bojun
Taleb, Tarik
Ji, Yuefeng
Optica Publishing Group
26.05.2023

Ruikun Wang, Jiawei Zhang, Shuangyi Yan, Chuidian Zeng, Hao Yu, Zhiqun Gu, Bojun Zhang, Tarik Taleb, and Yuefeng Ji, "Suspect fault screen assisted graph aggregation network for intra-/inter-node failure localization in ROADM-based optical networks," J. Opt. Commun. Netw. 15, C88-C99 (2023)

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© 2023 Optica Publishing Group. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1364/JOCN.480970
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https://urn.fi/URN:NBN:fi-fe20231010139434
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Abstract

In optical networks, failure localization is essential to stable operation and service restoration. Several approaches have been presented to achieve accurate failure localization of nodes and inter-nodes. However, due to increasing traffic and demand for flexibility, the reconfigurable optical add/drop multiplexer (ROADM) is evolving towards a multi-degree architecture. Therefore, each ROADM is composed of multiple devices, which makes intra-node failures become more complex. In this context, intra-node failure localization can effectively reduce the pressure on network operators to further find specific devices. In this work, we redefine the intra-/inter-node failure model for multi-degree ROADM-based optical networks and propose a suspect fault screen assisted graph aggregation network (SFS-GRN) for intra-/inter-node failure localization. The SFS is responsible for screening out suspect fault devices from all devices and reducing the number of candidate devices. The GRN is used to analyze these monitoring data from an optical performance monitoring (OPM) node and network wide and to determine the most likely failure device. The proposed scheme is evaluated in a nine-node simulated network and three-node testbed network. Extensive results show that the SFS-GRN achieves higher accuracy compared with existing methods under different percentages of OPM deployment, numbers of service requests, and failure types. The SFS can remove more than 98% of devices, which is beneficial to further detection and repair for network operators. Moreover, the proposed strategy takes about 10 ms to detect a potential failure, and it has the potential to be applied to a real scenario.

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