Abstract

In optical transport networks, the urgent demand for control efficiency and intelligence has become one of the most significant challenges for telecom operators. With the development in control technology, more attention has been paid to performance enhancement of the centralized controller of software-defined optical networks (SDONs). Meanwhile, machine learning (ML) is emerging as a promising technology to facilitate the intelligence of control planes in SDONs. Some research works have been conducted to use ML to solve problems in optical transport networks. However, it is still a challenge to deploy and use computing resources. On the one hand, computing resources can be deployed inside the centralized controller of an SDON to enable control layer artificial intelligence (AI). On the other hand, computing resources can also be deployed on the hardware board to enable on-board AI. The two-layer AI functions are able to meet different intelligent requirements in data and control layers in different scenarios. Therefore, coordination between them is an important issue. In this paper, a novel control architecture based on an SDON is proposed, and it can support control layer AI and on-board AI simultaneously. Particularly, on-board AI is proposed based on edge computing to support various ML applications. To evaluate the proposed architecture, we develop an experimental testbed and demonstrate a typical use case, i.e., alarm information prediction. Experimental results show that coordination and cross-layer optimization between control layer AI and on-board AI can be achieved. However, there is much space for research in this area, and we envision some open issues.

© 2019 Optical Society of America

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