Deep-RMSA: A Deep-Reinforcement-Learning Routing, Modulation and Spectrum Assignment Agent for Elastic Optical Networks

Chen, Xiaoliang; Guo, Jiannan; Zhu, Zuqing; Proietti, Roberto; Castro, Alberto; Yoo, S. J. B.

doi:10.1364/ofc.2018.w4f.2

Cited by 64 publications

(65 citation statements)

References 4 publications

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“…Chen et al demonstrate routing, modulation format, and spectrum assignment in EON with DRL in [82]. The action set of DRL agent are predefined resources assignment schemes.…”

Section: Reinforcement Learning-based Rsamentioning

confidence: 99%

Machine learning for intelligent optical networks: A comprehensive survey

Yang

2020

Journal of Network and Computer Applications

108

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With the rapid development of Internet and communication systems, both in services and technologies, communication networks have been suffering increasing complexity. It is imperative to improve intelligence in communication network, and several aspects have been incorporating with Artificial Intelligence (AI) and Machine Learning (ML).Optical network, which plays an important role both in core and access network in communication networks, also faces great challenges of system complexity and the requirement of manual operations. To overcome the current limitations and address the issues of future optical networks, it is essential to deploy more intelligence capability to enable autonomous and flexible network operations. ML techniques are proved to have superiority on solving complex problems; and thus recently, ML techniques have been used for many optical network applications.In this paper, a detailed survey of existing applications of ML for intelligent optical networks is presented. The applications of ML are classified in terms of their use cases, which are categorized into optical network control and resource management, and optical networks monitoring and survivability. The use cases are analyzed and compared according to the used ML techniques. Besides, a tutorial for ML applications is provided from the aspects of the introduction of common ML algorithms, paradigms of ML, and motivations of applying ML. Lastly, challenges and possible solutions of ML application in optical networks are also discussed, which intends to inspire future innovations in leveraging ML to build intelligent optical networks.

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“…Chen et al demonstrate routing, modulation format, and spectrum assignment in EON with DRL in [82]. The action set of DRL agent are predefined resources assignment schemes.…”

Section: Reinforcement Learning-based Rsamentioning

confidence: 99%

Machine learning for intelligent optical networks: A comprehensive survey

Yang

2020

Journal of Network and Computer Applications

108

View full text Add to dashboard Cite

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“…The authors in [17] present an RL agent that performs routing optimization by automatically adapting to current traffic conditions with the goal of minimizing the end-to-end latencies of all connections routed in the network. The authors in [18] present an RL agent for the cognitive and autonomous routing of lightpaths in elastic optical networks. The work in [19] proposes an RL-based routing policy for provisioning connectivity services with different QoS requirements.…”

Section: Literature Reviewmentioning

confidence: 99%

Reinforcement Learning for Slicing in a 5G Flexible RAN

Raza

Natalino

Öhlen

et al. 2019

J. Lightwave Technol.

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Network slicing enables an infrastructure provider (InP) to support heterogeneous 5G services over a common platform (i.e., by creating a customized slice for each service). Once in operation, slices can be dynamically scaled up/down to match the variation of their service requirements. An InP generates revenue by accepting a slice request. If a slice cannot be scaled up when required, an InP has to also pay a penalty (proportional to the level of service degradation). It becomes then crucial for an InP to decide which slice requests should be accepted/rejected in order to increase its net profit. This paper presents a slice admission strategy based on reinforcement learning (RL) in the presence of services with different priorities. The use case considered is a 5G flexible radio access network (RAN), where slices of different mobile service providers are virtualized over the same RAN infrastructure. The proposed policy learns which are the services with the potential to bring high profit (i.e., high revenue with low degradation penalty), and hence should be accepted. The performance of the RL-based admission policy is compared against two deterministic heuristics. Results show that in the considered scenario, the proposed strategy outperforms the benchmark heuristics by at least 55%. Moreover, this paper shows how the policy is able to adapt to different conditions in terms of: (i) slice degradation penalty vs. slice revenue factors, and (ii) proportion of high vs. low priority services.

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“…[11][12][13] The application of ML to assist in complex optical network management decisions has been shown to bring very promising advantages. 14,15 In dynamic network operation, ML algorithms have been used to model complex optical network operations such as service provisioning, [16][17][18] admission control, 19 and traffic prediction. 18,20 These ML algorithms have shown potential benefits towards supporting autonomous monitoring and operation of optical networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Experiment-based detection of service disruption attacks in optical networks using data analytics and unsupervised learning

Furdek

Natalino

Schiano

et al. 2019

Metro and Data Center Optical Networks and Short-Reach Links II

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The paper addresses the detection of malicious attacks targeting service disruption at the optical layer as a key prerequisite for fast and effective attack response and network recovery. We experimentally demonstrate the effects of signal insertion attacks with varying intensity in a real-life scenario. By applying data analytics tools, we analyze the properties of the obtained dataset to determine how the relationships among different optical performance monitoring (OPM) parameters of the signal change in the presence of an attack as opposed to the normal operating conditions. In addition, we evaluate the performance of an unsupervised learning technique, i.e., a clustering algorithm for anomaly detection, which can detect attacks as anomalies without prior knowledge of the attacks. We demonstrate the potential and the challenges of unsupervised learning for attack detection, propose guidelines for attack signature identification needed for the detection of the considered attack methods, and discuss remaining challenges related to optical network security.

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Deep-RMSA: A Deep-Reinforcement-Learning Routing, Modulation and Spectrum Assignment Agent for Elastic Optical Networks

Cited by 64 publications

References 4 publications

Machine learning for intelligent optical networks: A comprehensive survey

Machine learning for intelligent optical networks: A comprehensive survey

Reinforcement Learning for Slicing in a 5G Flexible RAN

Experiment-based detection of service disruption attacks in optical networks using data analytics and unsupervised learning

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