A SDN-based On-Demand Path Provisioning Approach across Multi-domain Optical Networks

The interconnection of remote datacentres with optical networks are emerging use cases and such orchestration of multi-domains require the design of new network control, management, and orchestration architectures. Such heterogeneity needs to adopt end-to-end services like on-demand path provisioning. It is acknowledged that such scenarios are more complexed and have fundamental limitations in terms of high performance and delay. To address these issues, and as a means to cope with the complexity growth, research in this area is considering the concept of Software-Defined Network (SDN) orchestration for multi-domain optical networks to coordinated the control of heterogeneous systems. This paper presents a SDN path provisioning approach across Multi-Domain Optical Networks. The aim is to develop an efficient on-demand path provisioning platform in a software defined optical network at the control plane to dynamically manage the network's load, especially in emergency scenarios. The proposed distributed system architecture will help to solve the longstanding problem of inter-domain path provisioning. Our proposed architecture is implemented and validated in a control plane testbed to validate the approach. The paper also evaluated the factors such Quality of Service (QoS) of the network deployment associated with delay or control overhead. Our results show that the method will reduce additional delays in a multi-domain optical network, where high capacity and low latency are requirements for data-intensive applications and cloud services. The proposed method also maintains the total number of flows as low as possible to make the algorithm fast and reduce overheads

Management of Software-Defined Networking Powered by Artificial Intelligence

Separating data and control planes by Software-Defined Networking (SDN) not only handles networks centrally and smartly. However, through implementing innovative protocols by centralized controllers, it also contributes flexibility to computer networks. The Internet-of-Things (IoT) and the implementation of 5G have increased the number of heterogeneous connected devices, creating a huge amount of data. Hence, the incorporation of Artificial Intelligence (AI) and Machine Learning is significant. Thanks to SDN controllers, which are programmable and versatile enough to incorporate machine learning algorithms to handle the underlying networks while keeping the network abstracted from controller applications. In this chapter, a software-defined networking management system powered by AI (SDNMS-PAI) is proposed for end-to-end (E2E) heterogeneous networks. By applying artificial intelligence to the controller, we will demonstrate this regarding E2E resource management. SDNMS-PAI provides an architecture with a global view of the underlying network and manages the E2E heterogeneous networks with AI learning

Computer-Mediated Communication

This book is an anthology of present research trends in Computer-mediated Communications (CMC) from the point of view of different application scenarios. Four different scenarios are considered: telecommunication networks, smart health, education, and human-computer interaction. The possibilities of interaction introduced by CMC provide a powerful environment for collaborative human-to-human, computer-mediated interaction across the globe

A framework for Traffic Engineering in software-defined networks with advance reservation capabilities

298 p.En esta tesis doctoral se presenta una arquitectura software para facilitar la introducción de técnicas de ingeniería de tráfico en redes definidas por software. La arquitectura ha sido diseñada de forma modular, de manera que soporte múltiples casos de uso, incluyendo su aplicación en redes académicas. Cabe destacar que las redes académicas se caracterizan por proporcionar servicios de alta disponibilidad, por lo que la utilización de técnicas de ingeniería de tráfico es de vital importancia a fin de garantizar la prestación del servicio en los términos acordados. Uno de los servicios típicamente prestados por las redes académicas es el establecimiento de circuitos extremo a extremo con una duración determinada en la que una serie de recursos de red estén garantizados, conocido como ancho de banda bajo demanda, el cual constituye uno de los casos de uso en ingeniería de tráfico más desafiantes. Como consecuencia, y dado que esta tesis doctoral ha sido co-financiada por la red académica GÉANT, la arquitectura incluye soporte para servicios de reserva avanzada. La solución consiste en una gestión de los recursos de red en función del tiempo, la cual mediante el empleo de estructuras de datos y algoritmos específicamente diseñados persigue la mejora de la utilización de los recursos de red a la hora de prestar este tipo de servicios. La solución ha sido validada teniendo en cuenta los requisitos funcionales y de rendimiento planteados por la red GÉANT. Así mismo, cabe destacar que la solución será utilizada en el despliegue piloto del nuevo servicio de ancho de banda bajo demanda de la red GÉANT a finales del 2017