{"title":"革命性的光网络:大型语言模型的集成和影响","authors":"Sergio Cruzes","doi":"10.1016/j.osn.2025.100812","DOIUrl":null,"url":null,"abstract":"<div><div>The increasing complexity and scale of optical networks demand advanced automation frameworks capable of adapting to dynamic service requirements, physical-layer impairments, and multi-vendor environments. Traditional solutions—based on static rule sets or narrowly scoped machine learning models—struggle to manage real-time performance, heterogeneous data, and domain-specific variability. Large Language Models (LLMs), built on transformer architectures, offer a paradigm shift by enabling context-aware reasoning, multi-task generalization, and natural language interpretation. These models can automate configuration generation, fault diagnosis, alarm correlation, and routing and spectrum assignment (RSA), while enhancing Quality of Transmission (QoT) estimation and scenario modeling.</div><div>This article provides a comprehensive survey of current automation approaches in optical networks, including software-defined networking (SDN), intent-based networking (IBN), machine learning (ML)-based orchestration, and cognitive control architectures. Special attention is given to emerging paradigms that integrate LLMs for intent interpretation, fault analysis, configuration generation, and reasoning.</div><div>Building on these foundations, we propose a hybrid framework that integrates LLMs with Digital Twin (DT) technologies to enable closed-loop control, predictive optimization, and explainable, intent-driven decision-making. Telemetry streams feed both DT simulations and LLM-based reasoning agents, supporting proactive reconfiguration and fault mitigation. To address LLM limitations—such as hallucinations and inference latency —the framework incorporates prompt engineering, retrieval-augmented generation (RAG), domain-specific fine-tuning, and simulation-based validation.</div><div>The proposed architecture paves the way for resilient, autonomous, and sustainable optical networks that can self-optimize and adapt in real time.</div></div>","PeriodicalId":54674,"journal":{"name":"Optical Switching and Networking","volume":"57 ","pages":"Article 100812"},"PeriodicalIF":1.9000,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Revolutionizing optical networks: The integration and impact of large language models\",\"authors\":\"Sergio Cruzes\",\"doi\":\"10.1016/j.osn.2025.100812\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The increasing complexity and scale of optical networks demand advanced automation frameworks capable of adapting to dynamic service requirements, physical-layer impairments, and multi-vendor environments. Traditional solutions—based on static rule sets or narrowly scoped machine learning models—struggle to manage real-time performance, heterogeneous data, and domain-specific variability. Large Language Models (LLMs), built on transformer architectures, offer a paradigm shift by enabling context-aware reasoning, multi-task generalization, and natural language interpretation. These models can automate configuration generation, fault diagnosis, alarm correlation, and routing and spectrum assignment (RSA), while enhancing Quality of Transmission (QoT) estimation and scenario modeling.</div><div>This article provides a comprehensive survey of current automation approaches in optical networks, including software-defined networking (SDN), intent-based networking (IBN), machine learning (ML)-based orchestration, and cognitive control architectures. Special attention is given to emerging paradigms that integrate LLMs for intent interpretation, fault analysis, configuration generation, and reasoning.</div><div>Building on these foundations, we propose a hybrid framework that integrates LLMs with Digital Twin (DT) technologies to enable closed-loop control, predictive optimization, and explainable, intent-driven decision-making. Telemetry streams feed both DT simulations and LLM-based reasoning agents, supporting proactive reconfiguration and fault mitigation. To address LLM limitations—such as hallucinations and inference latency —the framework incorporates prompt engineering, retrieval-augmented generation (RAG), domain-specific fine-tuning, and simulation-based validation.</div><div>The proposed architecture paves the way for resilient, autonomous, and sustainable optical networks that can self-optimize and adapt in real time.</div></div>\",\"PeriodicalId\":54674,\"journal\":{\"name\":\"Optical Switching and Networking\",\"volume\":\"57 \",\"pages\":\"Article 100812\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optical Switching and Networking\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1573427725000190\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optical Switching and Networking","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1573427725000190","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Revolutionizing optical networks: The integration and impact of large language models
The increasing complexity and scale of optical networks demand advanced automation frameworks capable of adapting to dynamic service requirements, physical-layer impairments, and multi-vendor environments. Traditional solutions—based on static rule sets or narrowly scoped machine learning models—struggle to manage real-time performance, heterogeneous data, and domain-specific variability. Large Language Models (LLMs), built on transformer architectures, offer a paradigm shift by enabling context-aware reasoning, multi-task generalization, and natural language interpretation. These models can automate configuration generation, fault diagnosis, alarm correlation, and routing and spectrum assignment (RSA), while enhancing Quality of Transmission (QoT) estimation and scenario modeling.
This article provides a comprehensive survey of current automation approaches in optical networks, including software-defined networking (SDN), intent-based networking (IBN), machine learning (ML)-based orchestration, and cognitive control architectures. Special attention is given to emerging paradigms that integrate LLMs for intent interpretation, fault analysis, configuration generation, and reasoning.
Building on these foundations, we propose a hybrid framework that integrates LLMs with Digital Twin (DT) technologies to enable closed-loop control, predictive optimization, and explainable, intent-driven decision-making. Telemetry streams feed both DT simulations and LLM-based reasoning agents, supporting proactive reconfiguration and fault mitigation. To address LLM limitations—such as hallucinations and inference latency —the framework incorporates prompt engineering, retrieval-augmented generation (RAG), domain-specific fine-tuning, and simulation-based validation.
The proposed architecture paves the way for resilient, autonomous, and sustainable optical networks that can self-optimize and adapt in real time.
期刊介绍:
Optical Switching and Networking (OSN) is an archival journal aiming to provide complete coverage of all topics of interest to those involved in the optical and high-speed opto-electronic networking areas. The editorial board is committed to providing detailed, constructive feedback to submitted papers, as well as a fast turn-around time.
Optical Switching and Networking considers high-quality, original, and unpublished contributions addressing all aspects of optical and opto-electronic networks. Specific areas of interest include, but are not limited to:
• Optical and Opto-Electronic Backbone, Metropolitan and Local Area Networks
• Optical Data Center Networks
• Elastic optical networks
• Green Optical Networks
• Software Defined Optical Networks
• Novel Multi-layer Architectures and Protocols (Ethernet, Internet, Physical Layer)
• Optical Networks for Interet of Things (IOT)
• Home Networks, In-Vehicle Networks, and Other Short-Reach Networks
• Optical Access Networks
• Optical Data Center Interconnection Systems
• Optical OFDM and coherent optical network systems
• Free Space Optics (FSO) networks
• Hybrid Fiber - Wireless Networks
• Optical Satellite Networks
• Visible Light Communication Networks
• Optical Storage Networks
• Optical Network Security
• Optical Network Resiliance and Reliability
• Control Plane Issues and Signaling Protocols
• Optical Quality of Service (OQoS) and Impairment Monitoring
• Optical Layer Anycast, Broadcast and Multicast
• Optical Network Applications, Testbeds and Experimental Networks
• Optical Network for Science and High Performance Computing Networks