An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, machine learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing, and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude this paper proposing new possible research directions

Cognition procedures for optical network design and optimization

Telecom carriers have to adapt their networks to accommodate a growing volume of users, services and traffic. Thus, they have to search a continuous maximization of efficiency and reduction in costs. This thesis identifies an opportunity to accomplish this aim by reducing operation margins applied in the optical link power budgets, in optical transport networks. From an operational perspective, margin reduction will lead to a fall of the required investments on transceivers in the whole transport network. Based on how human learn, a cognitive approach is introduced and evaluated to reduce the System Margin. This operation margin takes into account, among other constraints, the long-term ageing process of the network infrastructure. Telecom operators normally apply a conservative and fixed value established during the design and commissioning phases. The cognitive approach proposes a flexible and variable value, adapted to the network conditions. It is based on the case-based reasoning machine learning technique, which has been further developped. Novel learning schemes are presented and evaluated. The cognition solution proposes a new lower launched power guaranteeing the quality of service of the new incoming lightpath. It will lead to provide transmission power savings with appropiate success rates when applying the cognitive approach. To this end, it relies on transmission values applied in past and successful similar network situations. They are stored in a knowledge base or memory of the system. Moreover, regarding the knowledge base, a static and a dynamic approaches have been developped and presented. In the last case, five new dynamic learning algorithms are presented and evaluated. In the static context, savings in transmission power up to 48% are achieved and the resulting System Margin reduction. Furthermore, the dynamic renewal of the knowledge base improves mean savings in launched power up to 7% or 18% with respect to the static approach, depending on the path. Thus, the cognitive approach appears as useful to be applied in commercial optical transport networks with the aim of reducing the operational System Margin.Los operadores de telecomunicaciones tienen que adaptar constantemente sus redes para acoger el volumen creciente de usuarios, servicios y tráfico asociado. Han de buscar constantemente una maximización de la eficiencia en la operación, así como una reducción continua de costes. Esta tesis identifica una oportunidad para alcanzar este objetivo por medio de la reducción de los márgenes operacionales aplicados en los balances de potencia en una red óptica de transporte. Desde un punto de vista operacional, la reducción de márgenes operativos conlleva una optimización de las inversiones requeridas en transceivers, entre otros puntos. Así, basándonos en cómo aprendemos los humanos, se introduce y evalúa una aproximación cognitiva para reducir el System Margin. Este margen operativo se introduce en el balance de potencia, entre otros puntos, para compensar el proceso de envejecimiento a largo plazo de la infraestrcutura de red. Los operadores emplean normalmente un valor fijo y conservador, que se establece durante el diseño y comisionado de la red. Nuestra aproximación cognitiva propone en su lugar un valor flexible y variable, que se adapta a las condiciones de red actuales. Se basa en la técnica de machine learning conocida como case-based reasoning, que se desarrolla más profundamente. Se han propuesto y evaluado nuevos esquemas de aprendizaje. La solución cognitiva propone un nuevo valor más bajo de potencia transmitida, que garantiza la calidad de servicio requerida por el nuevo lighpath entrante. La propuesta logra ahorros en la potencia transmitida, a la vez que garantiza una tasa de éxito correcta cuando aplicamos esta solución cognitiva. Para ello, se apoya en la potencia transmitida en situaciones pasadas y similares a la actual, donde se transmitió una potencia que aseguró el correcto establecimiento del lighpath. Esta información se almacena en una base de conocimiento. En este sentido, se han desarrollado y presentado dos aproximaciones: una base de conocimiento estática y otra dinámica. En el caso del contexto dinámico, se han desarrollado y evaluado cinco nuevos algoritmos de aprendizaje. En el contexto estático, se consigue un ahorro en potencia de hasta un 48%, con la correspondiente reducción del System Margin. En el contexto dinámico, la actualización online de la base de conocimiento proporciona adicionalmente una ganancia en potencia transmitida con respecto a la aproximación estática de hasta un 7% o un 18%, dependiendo de la ruta. De esta forma se comprueba que la propuesta cognitiva se revela como útil y aplicable sobre una red óptica de transporte comercial con el objetivo de reducir el margen operativo conocido como System Margin.Postprint (published version

Cognition procedures for optical network design and optimization

Telecom carriers have to adapt their networks to accommodate a growing volume of users, services and traffic. Thus, they have to search a continuous maximization of efficiency and reduction in costs. This thesis identifies an opportunity to accomplish this aim by reducing operation margins applied in the optical link power budgets, in optical transport networks. From an operational perspective, margin reduction will lead to a fall of the required investments on transceivers in the whole transport network. Based on how human learn, a cognitive approach is introduced and evaluated to reduce the System Margin. This operation margin takes into account, among other constraints, the long-term ageing process of the network infrastructure. Telecom operators normally apply a conservative and fixed value established during the design and commissioning phases. The cognitive approach proposes a flexible and variable value, adapted to the network conditions. It is based on the case-based reasoning machine learning technique, which has been further developped. Novel learning schemes are presented and evaluated. The cognition solution proposes a new lower launched power guaranteeing the quality of service of the new incoming lightpath. It will lead to provide transmission power savings with appropiate success rates when applying the cognitive approach. To this end, it relies on transmission values applied in past and successful similar network situations. They are stored in a knowledge base or memory of the system. Moreover, regarding the knowledge base, a static and a dynamic approaches have been developped and presented. In the last case, five new dynamic learning algorithms are presented and evaluated. In the static context, savings in transmission power up to 48% are achieved and the resulting System Margin reduction. Furthermore, the dynamic renewal of the knowledge base improves mean savings in launched power up to 7% or 18% with respect to the static approach, depending on the path. Thus, the cognitive approach appears as useful to be applied in commercial optical transport networks with the aim of reducing the operational System Margin.Los operadores de telecomunicaciones tienen que adaptar constantemente sus redes para acoger el volumen creciente de usuarios, servicios y tráfico asociado. Han de buscar constantemente una maximización de la eficiencia en la operación, así como una reducción continua de costes. Esta tesis identifica una oportunidad para alcanzar este objetivo por medio de la reducción de los márgenes operacionales aplicados en los balances de potencia en una red óptica de transporte. Desde un punto de vista operacional, la reducción de márgenes operativos conlleva una optimización de las inversiones requeridas en transceivers, entre otros puntos. Así, basándonos en cómo aprendemos los humanos, se introduce y evalúa una aproximación cognitiva para reducir el System Margin. Este margen operativo se introduce en el balance de potencia, entre otros puntos, para compensar el proceso de envejecimiento a largo plazo de la infraestrcutura de red. Los operadores emplean normalmente un valor fijo y conservador, que se establece durante el diseño y comisionado de la red. Nuestra aproximación cognitiva propone en su lugar un valor flexible y variable, que se adapta a las condiciones de red actuales. Se basa en la técnica de machine learning conocida como case-based reasoning, que se desarrolla más profundamente. Se han propuesto y evaluado nuevos esquemas de aprendizaje. La solución cognitiva propone un nuevo valor más bajo de potencia transmitida, que garantiza la calidad de servicio requerida por el nuevo lighpath entrante. La propuesta logra ahorros en la potencia transmitida, a la vez que garantiza una tasa de éxito correcta cuando aplicamos esta solución cognitiva. Para ello, se apoya en la potencia transmitida en situaciones pasadas y similares a la actual, donde se transmitió una potencia que aseguró el correcto establecimiento del lighpath. Esta información se almacena en una base de conocimiento. En este sentido, se han desarrollado y presentado dos aproximaciones: una base de conocimiento estática y otra dinámica. En el caso del contexto dinámico, se han desarrollado y evaluado cinco nuevos algoritmos de aprendizaje. En el contexto estático, se consigue un ahorro en potencia de hasta un 48%, con la correspondiente reducción del System Margin. En el contexto dinámico, la actualización online de la base de conocimiento proporciona adicionalmente una ganancia en potencia transmitida con respecto a la aproximación estática de hasta un 7% o un 18%, dependiendo de la ruta. De esta forma se comprueba que la propuesta cognitiva se revela como útil y aplicable sobre una red óptica de transporte comercial con el objetivo de reducir el margen operativo conocido como System Margin

Quality of service in optical burst switching networks

Tese dout., Engenharia Electrónica e Computação, Universidade do Algarve, 2009Fundação para e Ciência e a Tecnologi

Wavelength assignment in optical burst switching networks using neuro-dynamic programming

Cataloged from PDF version of article.All-optical networks are the most promising architecture for building large-size, hugebandwidth transport networks that are required for carrying the exponentially increasing Internet traffic. Among the existing switching paradigms in the literature, the optical burst switching is intended to leverage the attractive properties of optical communications, and at the same time, take into account its limitations. One of the major problems in optical burst switching is high blocking probability that results from one-way reservation protocol used. In this thesis, this problem is solved in wavelength domain by using smart wavelength assignment algorithms. Two heuristic wavelength assignment algorithms prioritizing available wavelengths according to reservation tables at the network nodes are proposed. The major contribution of the thesis is the formulation of the wavelength assignment problem as a continuous-time, average cost dynamic programming problem and its solution based on neuro-dynamic programming. Experiments are done over various traffic loads, burst lengths, and number of wavelength converters with a pool structure. The simulation results show that the wavelength assignment algorithms proposed for optical burst switching networks in the thesis perform better than the wavelength assignment algorithms in the literature that are developed for circuit-switched optical networks.Keçeli, FeyzaM.S

Efficient Passive Clustering and Gateways selection MANETs

Passive clustering does not employ control packets to collect topological information in ad hoc networks. In our proposal, we avoid making frequent changes in cluster architecture due to repeated election and re-election of cluster heads and gateways. Our primary objective has been to make Passive Clustering more practical by employing optimal number of gateways and reduce the number of rebroadcast packets