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

Yajie Li,Jie Zhang,Yongli Zhao,Feng Wang,Zebin Zeng,Xiangjun Xin,Mingzhe Liu,Xinghua Li,Zhuotong Li

doi:10.1109/ojcs.2020.3030957

Yajie Li, Jie Zhang + Show 7 more

Open Access

https://doi.org/10.1109/ojcs.2020.3030957

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Abstract

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.

Highlights

With the continuous popularization and promotion of 5G technology, the emerging services in the network puts forward new requirements on the underlying transport network, such as low latency and large bandwidth transmission [1]
We introduce the idea of introducing on-board artificial intelligence (AI) and achieving the cloud-edge collaboration in this architecture
The deployment of AI in the optical network is conducive to improving network control capabilities

Summary

Introduction

With the continuous popularization and promotion of 5G technology, the emerging services in the network puts forward new requirements on the underlying transport network, such as low latency and large bandwidth transmission [1]. In order to solve the problem of uneven distribution of computing resources and hierarchical optimization, we introduced on-board AI to SOON [25], and proposed several cloud-edge collaboration modes to improve the network control capability. The collaboration of control layer AI and multiple on-board AI can effectively improve efficiency of model training and testing, and rationally use computing resources to provide rapid response to different application requirements.

Results

Conclusion