Cognitive, Heterogeneous and Reconfigurable Optical Networks: The CHRON Project

Caballero, Antonio; Borkowski, Robert; Miguel, Ignacio de; Durán, Ramón J.; Aguado, Juan Carlos; Fernández, Natalia; Jiménez, Tamara; Rodríguez, Isabelle; Sánchez, David; Lorenzo, Rubén M.; Klonidis, Dimitrios; Palkopoulou, Eleni; Diamantopoulos, Nikolaos-Panteleimon; Tomkos, Ioannis; Siracusa, Domenico; Francescon, Antonio; Salvadori, Elio; Ye, Yabin; Vizcaíno, Jorge López; Pittalà, Fabio; Tymecki, Andrzej; Monroy, Idelfonso Tafur

doi:10.1109/jlt.2014.2318994

Cited by 19 publications

(16 citation statements)

References 46 publications

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“…Cognitive networks have proved to be an excellent solution for wireless networks [3], and have also been recently proposed for optical networks [4], like the CHRON (Cognitive Heterogeneous Reconfigurable Optical Network) approach [5,6]. The CHRON architecture, which will be briefly presented in Section II, is based on a central computation entity, the Cognitive Decision System (CDS) [7], which is responsible of making decisions on how to configure devices and deal with traffic demands, taking into account the current network status.…”

Section: Abstract-cognition Dynamic Lightpath Establishment Emulatomentioning

confidence: 99%

“…It also configures the CMS monitoring campaigns over specific network parameters and triggers lightpath setup and teardown mechanisms by means of the GMPLS protocol family (included in the CMS). In this section, we provide an overview of the CHRON architecture, but additional details can be found on [5]. Fig.…”

Section: Chron Architecturementioning

confidence: 99%

“…The network can learn from these adaptations and use them to make future decisions, all while taking into account end-to-end goals" [2]. Cognitive networks involve the utilization of three types of elements: monitoring elements, which enable the network to be aware of current conditions; software adaptable elements, which enable the network to adapt to changing conditions; and cognitive processes, which learn or make use of past history to improve performance.Cognitive networks have proved to be an excellent solution for wireless networks [3], and have also been recently proposed for optical networks [4], like the CHRON (Cognitive Heterogeneous Reconfigurable Optical Network) approach [5,6]. The CHRON architecture, which will be briefly presented in Section II, is based on a central computation entity, the Cognitive Decision System (CDS) [7], which is responsible of making decisions on how to configure devices and deal with traffic demands, taking into account the current network status.…”

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Experimental assessment of a cognitive mechanism to reduce the impact of outdated TEDs in optical networks

et al. 2015

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We have recently proposed and demonstrated, by means of simulation, the benefits of a simple yet effective cognitive technique to enhance stateless Path Computation Element (PCE) algorithms with the aim of reducing the connection blocking probability when relying on a potentially non up-to-date Traffic Engineering Database (TED). In this paper, we employ that technique, called Elapsed Time Matrix (ETM), in the framework of the CHRON (Cognitive Heterogeneous Reconfigurable Optical Network) architecture and, more importantly, validate and analyse its performance in an emulation environment (rather than in a simulation environment) supporting impairment-aware lightpath establishment. Not only dynamic lightpath establishment on demand has been studied, but also restoration processes when facing optical link failures. Emulation results demonstrate that ETM reduces the blocking probability when establishing lightpaths on demand, and increases the percentage of successful restorations in case of optical link failure. Moreover, the use of that technique has little impact on lightpath setup time and lightpath restoration time, respectively. Keywords-cognition, dynamic lightpath establishment, emulator, outdated database traffic engineering database 1 IntroductionOptical transport networks are increasing their complexity due to the use of heterogeneous transmission technologies and the support of services with different requirements [1]. One solution to efficiently control such heterogeneous optical networks comes from the use of cognitive techniques [2]. 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" [2]. Cognitive networks involve the utilization of three types of elements: monitoring elements, which enable the network to be aware of current conditions; software adaptable elements, which enable the network to adapt to changing conditions; and cognitive processes, which learn or make use of past history to improve performance.Cognitive networks have proved to be an excellent solution for wireless networks [3], and have also been recently proposed for optical networks [4], like the CHRON (Cognitive Heterogeneous Reconfigurable Optical Network) approach [5,6]. The CHRON architecture, which will be briefly presented in Section II, is based on a central computation entity, the Cognitive Decision System (CDS) [7], which is responsible of making decisions on how to configure devices and deal with traffic demands, taking into account the current network status. As its name suggests, the CDS leverages on a learning process to improve its performance with acquired experience.When dealing with a lightpath establishment procedure, the CDS adopts a role similar to that of the Path Computation Element (PCE) [8], which has lately received increasing attention in the optical networking community. T...

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Section: Abstract-cognition Dynamic Lightpath Establishment Emulatomentioning

confidence: 99%

Section: Chron Architecturementioning

confidence: 99%

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Experimental assessment of a cognitive mechanism to reduce the impact of outdated TEDs in optical networks

et al. 2015

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“…In principle, a coherent receiver is a software-defined receiver, capable of acquiring and demodulating any modulation format for which appropriate algorithms are implemented. Simultaneously, optical networks are evolving towards dynamic lightpath switching driven by paradigms of self-managing and self-optimization [2]. For short-lived connections, lasting in the order of seconds, the delay incurred by the control plane may constitute a considerable overhead.…”

Section: Introductionmentioning

confidence: 99%

Clustering algorithms for Stokes space modulation format recognition

Boada¹,

Borkowski²,

Monroy³

2015

Opt. Express

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Stokes space modulation format recognition (Stokes MFR) is a blind method enabling digital coherent receivers to infer modulation format information directly from a received polarization-division-multiplexed signal. A crucial part of the Stokes MFR is a clustering algorithm, which largely influences the performance of the detection process, particularly at low signal-to-noise ratios. This paper reports on an extensive study of six different clustering algorithms: k-means, expectation maximization, density-based DBSCAN and OPTICS, spectral clustering and maximum likelihood clustering, used for discriminating between dual polarization: BPSK, QPSK, 8-PSK, 8-QAM, and 16-QAM. We determine essential performance metrics for each clustering algorithm and modulation format under test: minimum required signal-to-noise ratio, detection accuracy and algorithm complexity.

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“…Similarly, Gonzalez et al [10] demonstrate a novel cognitive receiver for modulation format recognition over radio-over-fiber links. Additionally, an European project known as Cognitive Heterogeneous Reconfigurable Optical Network (CHRON) developed a testbed of a cognitive network able to self-configure and self-optimize to efficiently use available resources [11]. As basis to perform and propose new optimization approaches in scenarios with all this complexity, it is crucial to have a simulation tool with proper and realistic optical network model able to provide an environment that is capable of simulating without physical resources limitations and able to integrate proof-of-concept features.…”

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confidence: 99%