Cognitive Dynamic Optical Networks [Invited]

Miguel, Ignacio de; Durán, Ramón J.; Jiménez, Tamara; Fernández, Natalia; Aguado, Juan Carlos; Lorenzo, Rubén M.; Caballero, Antonio; Monroy, Idelfonso Tafur; Ye, Yabin; Tymecki, Andrzej; Tomkos, Ioannis; Angelou, M.; Klonidis, Dimitrios; Francescon, Antonio; Siracusa, Domenico; Salvadori, Elio

doi:10.1364/jocn.5.00a107

Cited by 47 publications

(19 citation statements)

References 22 publications

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“…That KB is used by the agents when devising plans of action on how to succeed on decision-making, and can be updated in order to adapt to changing conditions. Along this line, holistic frameworks, like cognitive optical networks, which perceive, act, learn, adapt and optimize their performance, have been proposed by different researchers [13,14,15,16,17,18].…”

Section: An Overview Of Ai and Related Techniquesmentioning

confidence: 99%

“…Thus, process automation has been identified as a key enabler for driving down operational costs, and also to help move human intelligence away from repetitive tasks [135]. AI-based techniques are seen as a key enabler for automating network management operations [136,137], and cognitive optical networks were in fact proposed with that aim [13,14,15,16,17,18] but many open issues remain. The primary pre-requisite for automating network operations is the capability to efficiently collect and analyze telemetry information from the network.…”

Section: Automating Network Management Operationsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Mata

Miguel

Durán

et al. 2018

Optical Switching and Networking

Self Cite

309

171

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Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.

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Section: An Overview Of Ai and Related Techniquesmentioning

confidence: 99%

Section: Automating Network Management Operationsmentioning

confidence: 99%

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Mata

Miguel

Durán

et al. 2018

Optical Switching and Networking

Self Cite

309

171

View full text Add to dashboard Cite

show abstract

“…For the Q-factor estimation, in [47,48], a cognitive QoT estimator classifies lightpaths to highor low-quality categories. The classification method is case-based reasoning (CBR), which is based on the prior experiences or cases to make estimations.…”

Section: Ai-based Qot Modelingmentioning

confidence: 99%

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Liu

Lun

et al. 2020

Applied Sciences

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With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

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“…Service provisioning As stated, the Metro-Haul COM is responsible for the dynamic provisioning of services; from a control and management point of view, we have identified a set of basic services to be developed and on top of which main use cases will be demonstrated. [10]. Specifically, by applying data analytics to monitored data, control loops can be enabled, where analysis outcomes can be used to recommend network reconfiguration actions to the SDN controller.…”

Section: 1mentioning

confidence: 99%

“…Specifically, we assume in this example that the pool of HTTP servers need to be increased and the capacity of the VNT connecting the leaf cache to the users incremented; we assume that such VNT includes capacity for live-TV and for VoD contents. Hence, the CDN controller request the NFVO to scale the VNF by increasing the number of HTTP servers and incrementing the capacity of the VNT toward the users (5)(6)(7)(8)(9)(10). Finally, the availability of a new HTTP server is announced to the local cache manager, so that it can add that server to its local pool (11).…”

Section: 3mentioning

confidence: 99%

A control and management architecture supporting autonomic NFV services

et al. 2018

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The proposed Control, Orchestration and Management (COM) architecture is presented from a highlevel point of view; it enables the dynamic provisioning of services such as network data connectivity or generic network slicing instances based on Virtual Network Functions (VNF). The COM is based on Software Defined Networking (SDN) principles and is hierarchical, with a dedicated controller per technology domain. Along with the SDN control plane for the provisioning of connectivity, an ETSI NFV Management and Orchestration (MANO) system is responsible for the instantiation of Network Services, understood in this context as interconnected VNFs. A key, novel component of the COM architecture is the Monitoring and Data Analytics (MDA) system, able to collect monitoring data from the network, datacenters (DC) and applications which outputs can be used to proactively reconfigure resources thus adapting to future conditions, like load or degradations. To illustrate the COM architecture, a use case of a Content Delivery Network (CDN) network service taking advantage of the MDA ability to collect and deliver monitoring data is experimentally demonstrated.

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Cognitive Dynamic Optical Networks [Invited]

Cited by 47 publications

References 22 publications

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

A control and management architecture supporting autonomic NFV services

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