Juan Carlos Aguado

Dynamic Routing and Wavelength Assignment in Optical Networks by Means of Genetic Algorithms

We propose a novel genetic algorithm for solving the dynamic routing and wavelength assignment (DRWA) problem in wavelength-routed optical networks. The algorithm not only obtains low call blocking probability, but it also employs a very short computation time. Moreover, it is capable of providing fairness among connections, that is, to offer approximately the same quality of service (in terms of blocking probability) for all source-destination node pairs. Since requirements on optical network availability are highly severe, we also propose an extension of the algorithm to provide fault-tolerance capability at the optical layer. It is achieved by means of protection, where each optical connection request is provided with a pair of lightpaths (a primary and a backup lightpath). Again, the genetic algorithm proves to be highly efficient, in this case, at performing routing and wavelength assignment of pairs of lightpaths.

A Cognitive Quality of Transmission Estimator for Core Optical Networks

We propose a cognitive Quality of Transmission (QoT) estimator for classifying lightpaths into high or low quality categories in impairment-aware wavelength-routed optical networks. The technique is based on Case-Based Reasoning (CBR), an artificial intelligence technique which solves new problems by exploiting previous experiences, which are stored on a knowledge base. We also show that by including learning and forgetting techniques, the underlying knowledge base can be optimized, thus leading to a significant reduction on the computing time for on-line operation. The performance of the cognitive estimator is evaluated in a long haul and in an ultra-long haul network, and we demonstrate that it achieves more than 98% successful classifications, and that it is up to four orders of magnitude faster when compared with a non-cognitive QoT estimator, the Q-Tool.

Cognitive dynamic optical networks [invited]

The use of cognition is a promising element for the control of heterogeneous optical networks. Not only are cognitive networks able to sense current network conditions and act according to them, but they also take into account the knowledge acquired through past experiences; that is, they include learning with the aim of improving performance. In this paper, we review the fundamentals of cognitive networks and focus on their application to the optical networking area. In particular, a number of cognitive network architectures proposed so far, as well as their associated supporting technologies, are reviewed. Moreover, several applications, mainly developed in the framework of the EU FP7 Cognitive Heterogeneous Reconfigurable Optical Network (CHRON) project, are also described.

Cognition to design energetically efficient and impairment aware virtual topologies for optical networks

“Greening the Internet” is an important research topic in the last years. The Internet capacity and energy consumption have increased, and the utilization of design and operation techniques to reduce this consumption are a must. In this paper, we present a multiobjective genetic algorithm to design virtual topologies for reconfigurable wavelength-routed optical networks with the aim of reducing both the energy consumption and the network congestion while ensuring that the lightpaths of the virtual topologies fulfill quality of transmission requirements. Moreover, we also present another version of that method enhanced with cognitive techniques, and we show, by means of simulation, the performance advantages brought when introducing these cognitive techniques.

Multiobjective Genetic Algorithm to Design Cost-Efficient Wavelength-Routed Optical Networks

A new algorithm, GAPDELT, is presented to jointly design the resource provisioning and the logical topology of wavelength-routed optical networks. GAPDELT is based on genetic algorithms and uses Pareto optimality to reduce both the network congestion and the number of resources employed. By means of a simulation study, we show that GAPDELT designs a set of good alternatives that achieve up to 50% of reduction in terms of congestion or 75% of reduction in the number of transmitters and receivers required when compared with another well-known

Virtual topology reconfiguration in optical networks by means of cognition: Evaluation and experimental validation [invited]

In optical networking, virtual topologies have been introduced mainly to provide service providers with logical connections equipped with a reserved amount of bandwidth, which can be exploited to interconnect their equipment at the edges of the transport infrastructure. Virtual topologies are thus basically an abstraction of the real substrate, created by means of a process called virtual topology design (VTD). VTD is a complex task, affected by many parameters and constraints, and among them current traffic conditions are very relevant. Indeed, it is possible that after a certain time a virtual topology becomes inappropriate to serve current traffic. In such cases, the virtual topology can be reconfigured by creating new lightpaths or modifying or deleting existing ones, thus possibly creating some service interruptions. In this paper a new virtual topology reconfiguration technique is presented. In this technique, a cognitive entity designs and reconfigures virtual topologies by exploiting traffic forecasting solutions and taking advantage of past history. Moreover, a new transition method is also proposed to reduce the impact of instable routing tables during the reconfiguration process. We demonstrate, by means of simulation, the advantages of the proposed methods, as they reduce both the operational costs and the resources in operation while maintaining low packet loss ratio. Furthermore, we validate the operation of the proposed solutions in an emulated testbed.

An auto-tuning PID control system based on genetic algorithms to provide delay guarantees in Passive Optical Networks

Abstract Passive Optical Networks (PONs) are the most important access architectures since their deployment is massive all around the world. However, the QoS (Quality of Service) and the efficient management of the network resources have become the key point, especially with the new emerging services and applications. In particular, the delay and the bandwidth are becoming important limiting factors for the user experience. As a consequence, in a previous research we proposed the implementation of PID (Proportional-Integral-Derivative) control strategies to manage these networks parameters in PONs, demonstrating higher efficiency and more robustness than other previous existing algorithms. It is worth emphasizing that this is the first time to apply this control strategy in PONs access networks. However, in this paper we improve the PID control strategy by automating the tuning process with a genetic algorithm. Indeed, we have developed a novel automatic tuning technique based on genetic algorithms to tune a P controller that provides delay guarantees. Simulation results show that the control strategy reduces the tuning time up to 64% in comparison with the Ziegler–Nichols manual technique (ZN). On the other hand, it is demonstrated that our proposal is more accurate and robust that ZN since the genetic algorithm automatically evolves to the best solutions of the tuning parameters in contrast to the manual experiments required for the ZN method. Furthermore, we have complemented the use of the P controller with a new dynamic Admission Control (AC) module. This module implements a policy to selectively transmit or drop packets and leads a better delay control. The simulation analysis reveals that the real time evolution of the delay with the dynamic AC is more stable when compare to a conventional and simple fixed AC, reaching differences near one order of magnitude in the delay fluctuations.

Experimental demonstration of a cognitive quality of transmission estimator for optical communication systems

We report on the experimental performance of a case-based reasoning technique to predict whether optical channels fulfill quality of transmission requirements, thus supporting impairment-aware networking. Validation is performed in a WDM 80 Gb/s PDM-QPSK testbed.

A cognitive system for fast Quality of Transmission estimation in core optical networks

A cognitive system is proposed to quickly estimate the Quality of Transmission (QoT) of lightpaths in optical networks. It achieves more than 99% successful classifications of lightpaths into high or low QoT categories.

Nature-Inspired Routing and Wavelength Assignment Algorithms for Optical Circuit-Switched Polymorphic Networks

Polymorphic optical networks simultaneously support several optical switching paradigms over a single physical network. In this way, they provide service differentiation at the optical layer by employing the most appropriate paradigm for each service. One type of such architecture is the optical circuit-switched polymorphic network (OCSPN), which combines optical circuit switching paradigms with different grades of dynamism. The performance of this network relies on the utilization of efficient routing and wavelength assignment (RWA) algorithms. In this article, we review the fundamentals of OCSPNs and present a set of efficient RWA algorithms based on ant colony optimization and genetic algorithms.