DL-Assisted Cross-Layer Orchestration in Software-Defined IP-Over-EONs: From Algorithm Design to System Prototype

Liu, Siqi; Niu, Bin; Li, Deyun; Wang, Min; Tang, Shaofei; Kong, Jawei; Li, Baojia; Xie, Xiaokang; Zhu, Zuqing

doi:10.1109/jlt.2019.2925312

Cited by 23 publications

(1 citation statement)

References 42 publications

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“…However, the proper deployment of AI in SDON is still an open issue. A few AI-based network control and management schemes are designed to facilitate AI-assisted network automation in software-defined elastic optical network [22]- [23]. These schemes analyze network data to predict network status and implement automated management decisions.…”

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confidence: 99%

Self-Optimizing Optical Network With Cloud-Edge Collaboration: Architecture and Application

Zhao

et al. 2020

IEEE Open J. Comput. Soc.

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As an important bearer network of the fifth generation (5G) mobile communication technology, the optical transport network (OTN) needs to have high-quality network performance and management capabilities. Proof by facts, the combination of artificial intelligence (AI) technology and software-defined networking (SDN) can improve significant optimization effects and management for optical transport networks. However, how to properly deploy AI in optical networks is still an open issue. The training process of AI models depends on a large amount of computing resources and training data, which undoubtedly increases the carrying burden and operating costs of the centralized network controller. With the continuous upgrading of functions and performance, small AI-based chips can be used in optical networks as on-board AI. The emergence of edge computing technology can effectively relieve the computation load of network controllers and provide high-quality AI-based networks optimization functions. In this paper, we describe an architecture called self-optimizing optical network (SOON) with cloud-edge collaboration, which introduces control-layer AI and on-board AI to achieve intelligent network management. In addition, this paper introduces several cloud-edge collaborative strategies and reviews some AI-based network optimization applications to improve the overall network performance.

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confidence: 99%

Self-Optimizing Optical Network With Cloud-Edge Collaboration: Architecture and Application

Zhao

et al. 2020

IEEE Open J. Comput. Soc.

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Intent defined optical network with artificial intelligence-based automated operation and maintenance

Yang

Zhan

Yao

et al. 2020

Sci. China Inf. Sci.

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Energy efficient routing and lightpath management in software defined networking based inter-DC elastic optical networks

Panda¹

2020

Optical Fiber Technology

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DL-Assisted Cross-Layer Orchestration in Software-Defined IP-Over-EONs: From Algorithm Design to System Prototype

Cited by 23 publications

References 42 publications

Self-Optimizing Optical Network With Cloud-Edge Collaboration: Architecture and Application

Self-Optimizing Optical Network With Cloud-Edge Collaboration: Architecture and Application

Intent defined optical network with artificial intelligence-based automated operation and maintenance

Energy efficient routing and lightpath management in software defined networking based inter-DC elastic optical networks

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