面向智算中心间互联的光网络关键技术研究

李韫瑄; 杨亚萍; 涂佳一; 顾仁涛; 纪越峰

doi:10.11959/j.issn.1000-0801.2025106

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面向智算中心间互联的光网络关键技术研究

专题：智算光互联 | 更新时间：2025-10-31

- 面向智算中心间互联的光网络关键技术研究
- Research on key technologies of optical networks for interconnection between artificial intelligent data centers
- 电信科学 2025年41卷第4期页码：3-19
- 作者机构：
  
  1.北京邮电大学信息与通信工程学院，北京 100876
  2.北京邮电大学信息光子学与光通信全国重点实验室，北京 100876
- 作者简介：
  
  [ "李韫瑄（1998- ），女，北京邮电大学信息与通信工程学院博士生，主要研究方向为IP/光网络和智算中心光网络。" ]
  [ "杨亚萍（2002- ），女，北京邮电大学信息与通信工程学院硕士生，主要研究方向为智算中心光网络。" ]
  [ "涂佳一（2002- ），女，北京邮电大学信息与通信工程学院硕士生，主要研究方向为智算中心光网络。" ]
  [ "顾仁涛（1983- ），男，北京邮电大学信息与通信工程学院教授、博士生导师，主要研究方向为智能信息网络和智算光网络。" ]
  [ "纪越峰（1960- ），男，北京邮电大学信息与通信工程学院教授、博士生导师，北京邮电大学信息光子学与光通信全国重点实验室副主任，主要研究方向为宽带通信网络和光通信技术。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62271078);北京市自然科学基金资助项目(L247024)
- DOI：10.11959/j.issn.1000-0801.2025106
  中图分类号： TP393
- 收稿：2025-02-10，
  
  修回：2025-04-08，
  
  纸质出版：2025-04-20
- 稿件说明：
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李韫瑄,杨亚萍,涂佳一等.面向智算中心间互联的光网络关键技术研究[J].电信科学,2025,41(04):3-19.

LI Yunxuan,YANG Yaping,TU Jiayi,et al.Research on key technologies of optical networks for interconnection between artificial intelligent data centers[J].Telecommunications Science,2025,41(04):3-19.
李韫瑄,杨亚萍,涂佳一等.面向智算中心间互联的光网络关键技术研究[J].电信科学,2025,41(04):3-19. DOI： 10.11959/j.issn.1000-0801.2025106.

LI Yunxuan,YANG Yaping,TU Jiayi,et al.Research on key technologies of optical networks for interconnection between artificial intelligent data centers[J].Telecommunications Science,2025,41(04):3-19. DOI： 10.11959/j.issn.1000-0801.2025106.

摘要

智算中心作为大模型应用的关键算力基础设施，其高效运行依赖于高性能的光网络传输底座。然而，智算中心间光网络面临包括高实时性、高突发性和高可靠性的三重互联需求带来的诸多技术挑战。基于此，需要以高实时资源分配技术，助力智算光网络资源分配突破局部调度限制，以应对业务传输与调度时延挑战；以自适应协同优化技术，引导网络从被动调整向主动协同持续发展，以应对业务动态高突发性挑战；以前摄性故障恢复技术，推动网络从被动恢复故障向主动干预有序演进，以应对智算光网络的高可靠性挑战。展望未来，高实时大规模调度技术、算网深度协同技术和智算数字孪生技术，将为智算中心间互联的进一步发展提供新的机遇。

Abstract

As a critical computing infrastructure for large-scale model applications

the efficient operation of artificial intelligence data center relies on high-performance optical network transmission infrastructure. However

the optical networks interconnecting artificial intelligence data center face numerous challenges in meeting the demands of high real-time

high burstiness

and high reliability. Based on this

real-time resource allocation aims to overcome localized scheduling limitations in optical networks interconnecting artificial intelligence data center and reduce transmission and scheduling delays. Adaptive and collaborative optimization enables a continuous progression from passive adjustment to active collaboration in response to dynamic traffic bursts. Proactive failure recovery aims to achieve an orderly evolution from passive restoration to active intervention for reliability in optical networks interconnecting artificial intelligence data center. Future developments in large-scale real-time scheduling

deep computing-network convergence

and AI digital twins will drive new advancements in artificial intelligence data center interconnections.

关键词

Keywords

references

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