AlarmGPT: an intelligent alarm analyzer for optical networks using a generative pre-trained transformer

Yidi Wang, Chunyu Zhang, Jin Li, Yue Pang, Lifang Zhang, Min Zhang, and Danshi Wang

Author Information

Author Affiliations

Yidi Wang,¹Chunyu Zhang,¹Jin Li,¹Yue Pang,¹Lifang Zhang,²Min Zhang,¹and Danshi Wang^1,*

¹State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China

²The Intelligent Network Innovation Center, China Unicom, Beijing 100033, China

^*Corresponding author:danshi_wang@bupt.edu.cn

ORCID

YidiWang	https://orcid.org/0009-0009-7237-4267
ChunyuZhang	https://orcid.org/0009-0003-3254-9831
JinLi	https://orcid.org/0000-0003-1236-4613
MinZhang	https://orcid.org/0000-0002-8230-300X
DanshiWang	https://orcid.org/0000-0001-9815-4013

Find other works by these authors

• Y Wang
• C Zhang
• J Li
• Y Pang
• L Zhang
• M Zhang
• D Wang

Your library or personal account may give you access

• Abstract
• Full Article

• Figures (11)
• Tables (3)
• Equations (3)
• References (27)
• Cited By(2)
• Back to Top

Abstract

The proliferating development of optical networks has broadened the network scope and caused a corresponding rise in equipment deployment. This growth potentially results in a significant number of alarms in the case of equipment malfunctions or broken fiber. Managing these alarms efficiently and accurately has always been a critical concern within the research and industry community. The alarm processing workflow typically includes filtration, analysis, and diagnostic stages. In current optical networks, these procedures are often performed by experienced engineers, utilizing their expert knowledge and extensive experience. This method requires considerable human resources and time, as well as demanding proficiency prerequisites. To address this issue, we propose an intelligent alarm analysis assistant, “AlarmGPT,” for optical networks, utilizing a generative pre-trained transformer (GPT) and LangChain. The proposed AlarmGPT exhibits a high level of semantic comprehension and contextual awareness of alarm data, significantly enhancing the model’s ability of interpreting, classifying, and solving alarm events. Through verification of extensive alarm data collected from real optical transport networks (OTNs), the usability of AlarmGPT has been validated in the tasks of alarm knowledge Q&A, alarm compression, alarm priority analysis, and alarm diagnosis. This method has the potential to significantly reduce the labor and time required for alarm processing, while also lowering the experiential requisites incumbent upon network operators.

© 2024 Optica Publishing Group

Large language model-based optical network log analysis using LLaMA2 with instruction tuning

Yue Pang, Min Zhang, Yanli Liu, Xiangbin Li, Yidi Wang, Yahang Huan, Zhuo Liu, Jin Li, and Danshi Wang
J. Opt. Commun. Netw.16(11) 1116-1132 (2024)

OptiComm-GPT: a GPT-based versatile research assistant for optical fiber communication systems

Xiaotian Jiang, Min Zhang, Yuchen Song, Yao Zhang, Yidi Wang, Cheng Ju, and Danshi Wang
Opt. Express32(12) 20776-20796 (2024)

Intent-based control and management framework for optical transport networks supporting B5G services empowered by large language models [Invited]

Anna Tzanakaki, Markos Anastasopoulos, and Victoria-Maria Alevizaki
J. Opt. Commun. Netw.17(1) A112-A123 (2025)

Semi-supervised learning model synergistically utilizing labeled and unlabeled data for failure detection in optical networks

Zhiming Sun, Chunyu Zhang, Min Zhang, Bing Ye, and Danshi Wang
J. Opt. Commun. Netw.16(5) 541-552 (2024)

Machine learning models for alarm classification and failure localization in optical transport networks

Jatin Babbar, Ahmed Triki, Reda Ayassi, and Maxime Laye
J. Opt. Commun. Netw.14(8) 621-628 (2022)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (11)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (3)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (3)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription