A Cognitive Quality of Transmission Estimator for Core Optical Networks

Jiménez, Tamara; Aguado, Juan Carlos; Miguel, Ignacio de; Durán, Ramón J.; Angelou, M.; Merayo, Noemí; Fernández, Patricia; Lorenzo, Rubén M.; Tomkos, Ioannis; Abril, Evaristo J.

doi:10.1109/jlt.2013.2240257

Cited by 62 publications

(41 citation statements)

References 20 publications

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“…In particular, we have demonstrated a simple case of lightpath restoration in a 4 nodes testbed, where by exploiting cognitive techniques, a reduction of up to 48% restoration time can be achieved. Nevertheless, the performance evaluation of the cognitive engine in realistic networks using a large number of nodes has been shown in [4].…”

Section: Discussionmentioning

confidence: 99%

“…Hence, rather than implementing the whole CDS as a monolithic module, it is divided into different modules, each offering a functionality (or a set of related functionalities), and all of them exploiting cognition. The advantages of the CDS have been shown in the past in several network topologies [4] [4]. However, the CDS has been only validated in simulation mode.…”

Section: Introduction To Cognitive Reconfigurable Optical Networkmentioning

confidence: 99%

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Experimental Demonstration of a Cognitive Optical Network for Reduction of Restoration Time

Kachris

Klonidis

Francescon

et al. 2014

Optical Fiber Communication Conference

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This paper presents the implementation and performance evaluation of a cognitive heterogeneous optical network testbed. The testbed integrates the CMP, the data plane and the cognitive system and reduces by 48% the link restoration time. Introduction to Cognitive Reconfigurable Optical NetworksOptical networks are facing increased levels of heterogeneity, from types of services to transmission technologies. Hence, a key issue of highly heterogeneous networks is how to efficiently control and manage network resources while fulfilling user demands and complying with quality of service requirements. A solution for such a scenario may come from cognitive networks. A cognitive network is defined as "A network with a process that can perceive current network conditions, and then plan, decide, and act on those conditions. The network can learn from these adaptations and use them to make future decisions, all while taking into account end-to-end goals." [1]. Hence, there are three main ingredients in such a network:  Monitoring elements, which provide the network with the perception of the current conditions, and thus enable an aware network.  Software adaptable elements, which provide the network with the capacity of modifying its current configuration, thus enabling an adaptive network.  Cognitive processes, which learn or make use of past history, so that even when facing two equivalent scenarios, the network (or the element containing those cognitive processes) may act in a different way if its previous history is different. This third element is the main feature which enables a cognitive network. Therefore, a cognitive network is a network which is able to adapt itself to current or forecasted conditions by taking into account previous history, and which is able to act proactively, rather than reactively, in order to avoid problems before they arise. Moreover, those tasks should be performed autonomously, with little or no intervention of the network operator. Cognitive networks have already shown their advantages in wireless environments [2], but they are also applicable to wired communication architectures, and are especially appealing for optimizing performance in heterogeneous networks. The design of a cognitive optical network involves determining how the three aforementioned ingredients are implemented (and where), how they are glued together, as well as determining which tasks are going to be solved with the help of cognition.The CHRON project [3] has proposed a novel architecture for cognitive heterogeneous reconfigurable optical networks. The aim of the CHRON project is to develop and showcase network architecture and a control plane which efficiently use resources in a heterogeneous scenario while fulfilling QoS requirements of each type of services and applications supported by the network. This cognitive network observes acts, learns and optimizes its performance, taking into account its high degree of heterogeneity with respect to QoS, transmission and switching techniques. For that aim, ...

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Section: Discussionmentioning

confidence: 99%

Section: Introduction To Cognitive Reconfigurable Optical Networkmentioning

confidence: 99%

Experimental Demonstration of a Cognitive Optical Network for Reduction of Restoration Time

Kachris

Klonidis

Francescon

et al. 2014

Optical Fiber Communication Conference

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“…Related works exist in the literature that examine the inferential QoT framework by either focusing on designing a Software Defined Network (SDN) platform capable of supporting data-driven QoT decisions or by proposing datadriven approaches for accurate QoT decisions [5], [6], [7], [8]. Specifically, authors in [5] proposed a transport SDN architecture, and presented new data for devices, network elements, and SDN applications, in order to enable optical networks to support new services and virtualization with flexibility and scalability.…”

Section: Related Workmentioning

confidence: 99%

A data-driven QoT decision approach for multicast connections in metro optical networks

Panayiotou

Ellinas

Chatzis

2016

2016 International Conference on Optical Network Design and Modeling (ONDM)

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A data-driven technique for analyzing Quality-of-Transmission (QoT) data of previously established connections is proposed for accurately deciding the QoT of the newly arriving multicast requests in metro optical networks. The proposed approach is self-adaptive, it is a function of data that are independent from the physical layer impairment (PLIs) and thus does not require specific measurement equipment, and it does not assume the existence of a system with extensive processing and storage capabilities. It is also fast in processing new data, and fast in finding a near-accurate QoT model provided that such a model exists. The proposed technique can replace the existing Q-factor models that are not self-adaptive, they are a function of the PLIs, and their evaluation requires timeconsuming simulations, lab experiments, specific measurement equipment, and considerable human effort. The proposed datadriven QoT approach is based on the utilization of a feed-forward neural network that is trained on a dataset previously generated from a known Q-factor model. The dataset fed to the neural network is represented in a way that specifically describes the QoT of the multicast connections requesting to be established in the network but it is independent from the PLIs. The validity of the proposed approach is examined for two distinct networks, exhibiting a high accuracy when compared to the results of the Q-factor model utilized for generating the QoT data.

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“…The CBR approach achieves more than 99% successful classifications of optical connections, and is much faster for on-line operation than an existing non-cognitive approach, thus demonstrating the advantages of cognition. In [12] the basic cognitive QoT estimator was enhanced by including learning and forgetting capabilities. These capabilities are used to optimize the set of experiences stored in the knowledge base, and thus to improve performance accelerating the classification procedure by an order of magnitude.…”

Section: Quality-of-transmission Estimatormentioning

confidence: 99%

Performance monitoring techniques supporting cognitive optical networking

Caballero

Borkowski

Zibar

et al. 2013

2013 15th International Conference on Transparent Optical Networks (ICTON)

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High degree of heterogeneity of future optical networks, such as services with different quality-of-transmission requirements, modulation formats and switching techniques, will pose a challenge for the control and optimization of different parameters. Incorporation of cognitive techniques can help to solve this issue by realizing a network that can observe, act, learn and optimize its performance, taking into account end-to-end goals.In this letter we present the approach of cognition applied to heterogeneous optical networks developed in the framework of the EU project CHRON: Cognitive Heterogeneous Reconfigurable Optical Network. We focus on the approaches developed in the project for optical performance monitoring, which enable the feedback from the physical layer to the cognitive decision system by providing accurate description of the performance of the established lightpaths. Keywords: optical networks, cognition, dynamic optical networks, optical performance monitoring. INTRODUCTIONOptical networks are nowadays becoming more heterogeneous, ranging from different types of services, switching paradigms to physical interfaces. Therefore, network operators are facing the challenge of supporting a plethora of services, each with individual requirements on quality of service (QoS). Operators have also available different transmission technologies for their optical transport networks, such as coding, modulation formats or data rates. Moreover, in the short and medium term, optical networks may simultaneously support different switching paradigms such as semi-static and dynamic circuit switching. Hence, a key issue of highly heterogeneous networks is how to efficiently control and manage network resources while fulfilling user demands and complying with QoS requirements. A solution for such a scenario may come from cognitive networks. A cognitive network is defined as "a network with a process that can perceive current network conditions, and then plan, decide and act on those conditions. The network can learn from these adaptations and use them to make future decisions, all while taking into account end-to-end goals" [1]. Hence, a cognitive network should provide better end-to-end performance than a non-cognitive network. Cognitive paradigm have already shown to be a promising solution for wireless networks [1,2]. Cognition is also applicable to optical communication architectures [3][4][5], since it can offer flexibility to telecom operators by optimizing simultaneously physical layer components' characteristics (modulation format, forward error correction -FEC, wavelength capacity, etc.) and network layer parameters (bandwidth, number of simultaneous lightpaths, QoS, etc.) depending on application or service requirements.The aim of CHRON is to develop a showcase network architecture and a control plane that efficiently uses resources in a heterogeneous scenario, while fulfilling the QoS requirements of each type of services. To achieve this goal, CHRON relies on cognition, so that control decisions mus...

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A Cognitive Quality of Transmission Estimator for Core Optical Networks

Cited by 62 publications

References 20 publications

Experimental Demonstration of a Cognitive Optical Network for Reduction of Restoration Time

Experimental Demonstration of a Cognitive Optical Network for Reduction of Restoration Time

A data-driven QoT decision approach for multicast connections in metro optical networks

Performance monitoring techniques supporting cognitive optical networking

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