Software Defined Applications in Cellular and Optical Networks

abstract: Small wireless cells have the potential to overcome bottlenecks in wireless access through the sharing of spectrum resources. A novel access backhaul network architecture based on a Smart Gateway (Sm-GW) between the small cell base stations, e.g., LTE eNBs, and the conventional backhaul gateways, e.g., LTE Servicing/Packet Gateways (S/P-GWs) has been introduced to address the bottleneck. The Sm-GW flexibly schedules uplink transmissions for the eNBs. Based on software defined networking (SDN) a management mechanism that allows multiple operator to flexibly inter-operate via multiple Sm-GWs with a multitude of small cells has been proposed. This dissertation also comprehensively survey the studies that examine the SDN paradigm in optical networks. Along with the PHY functional split improvements, the performance of Distributed Converged Cable Access Platform (DCCAP) in the cable architectures especially for the Remote-PHY and Remote-MACPHY nodes has been evaluated. In the PHY functional split, in addition to the re-use of infrastructure with a common FFT module for multiple technologies, a novel cross functional split interaction to cache the repetitive QAM symbols across time at the remote node to reduce the transmission rate requirement of the fronthaul link has been proposed.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Dynamic Virtual Network Reconfiguration Over SDN Orchestrated Multitechnology Optical Transport Domains

Network virtualization is an emerging technique that enables multiple tenants to share an underlying physical infrastructure, isolating the traffic running over different virtual infrastructures/tenants. This technique aims to improve network utilization, while reducing the complexities in terms of network management for operators. Applied to this context, software defined networking (SDN) paradigm can ease network configurations by enabling network programmability and automation, which reduces the amount of operations required from both service and infrastructure providers. SDN techniques are decreasing vendor lock-in issues due to specific configuration methods or protocols. Application-based Network Operations (ABNO) is a toolbox of key network functional components with the goal of offering application-driven network management. Service provisioning using ABNO may involve direct configuration of data plane elements or delegate it to several control plane modules. We validate the applicability of ABNO to multi-tenant virtual networks in multi-technology optical domains based on two scenarios, in which multiple control plane instances are orchestrated by the architecture. Congestion Detection and Failure Recovery, are chosen to demonstrate fast recalculation and reconfiguration, while hiding the configurations in the physical layer from the upper layer.Grant numbers : supported by the Spanish Ministry of Economy and Competitiveness through the project FARO (TEC2012-38119)

Orchestration of IT/Cloud and Networks: From Inter-DC Interconnection to SDN/NFV 5G Services

The so-called 5G networks promise to be the foundations for the deployment of advanced services, conceived around the joint allocation and use of heterogeneous resources,including network, computing and storage. Resources are placed on remote locations constrained by the different service requirements, resulting in cloud infrastructures (as pool of resources) that need to be interconnected. The automation of the provisioning of such services relies on a generalized orchestra tion, defined as to the coherent coordination of heterogeneous systems, applied to common cases such as involving heterogeneous network domains in terms of control or data plane technologies, or cloud and network resources. Although cloud-computing platforms do take into account the need to interconnect remote virtual machine instances, mostly rely on managing L2 overlays over L3 (IP). The integration with transport networks is still not fully achieved, including leveraging the advances in software defined networks and transmission. We start with an overview of network orchestration, considering different models; we extend them to take into account cloud manage ment while mentioning relevant existing initiatives and conclude with the NFV architecture

Network service chaining using segment routing in multi-layer networks

Network service chaining, originally conceived in the network function virtualization (NFV) framework for software defined networks (SDN), is becoming an attractive solution for enabling service differentiation enforcement to microflows generated by data centers, 5G fronthaul and Internet of Things (IoT) cloud/fog nodes, and traversing a metro-core network. However, the current IP/MPLS-over optical multi-layer network is practically unable to provide such service chain enforcement. First, MPLS granularity prevents microflows from being conveyed in dedicated paths. Second, service configuration for a huge number of selected flows with different requirements is prone to scalability concerns, even considering the deployment of a SDN network. In this paper, effective service chaining enforcement along traffic engineered (TE) paths is proposed using segment routing and extended traffic steering mechanisms for mapping micro-flows. The proposed control architecture is based on an extended SDN controller encompassing a stateful path computation element (PCE) handling microflow computation and placement supporting service chains, whereas segment routing allows automatic service enforcement without the need for continuous configuration of the service node. The proposed solution is experimentally evaluated in segment routing over an elastic optical network (EON) network testbed with a deep packet inspection service supporting dynamic and automatic flow enforcement using Border Gateway Protocol with Flow Specification (BGP Flowspec) and OpenFlow protocols as alternative traffic steering enablers. Scalability of flow computation, placement, and steering are also evaluated showing the effectiveness of the proposed solution

Distributed collaborative knowledge management for optical network

Network automation has been long time envisioned. In fact, the Telecommunications Management Network (TMN), defined by the International Telecommunication Union (ITU), is a hierarchy of management layers (network element, network, service, and business management), where high-level operational goals propagate from upper to lower layers. The network management architecture has evolved with the development of the Software Defined Networking (SDN) concept that brings programmability to simplify configuration (it breaks down high-level service abstraction into lower-level device abstractions), orchestrates operation, and automatically reacts to changes or events. Besides, the development and deployment of solutions based on Artificial Intelligence (AI) and Machine Learning (ML) for making decisions (control loop) based on the collected monitoring data enables network automation, which targets at reducing operational costs. AI/ML approaches usually require large datasets for training purposes, which are difficult to obtain. The lack of data can be compensated with a collective self-learning approach. In this thesis, we go beyond the aforementioned traditional control loop to achieve an efficient knowledge management (KM) process that enhances network intelligence while bringing down complexity. In this PhD thesis, we propose a general architecture to support KM process based on four main pillars, which enable creating, sharing, assimilating and using knowledge. Next, two alternative strategies based on model inaccuracies and combining model are proposed. To highlight the capacity of KM to adapt to different applications, two use cases are considered to implement KM in a purely centralized and distributed optical network architecture. Along with them, various policies are considered for evaluating KM in data- and model- based strategies. The results target to minimize the amount of data that need to be shared and reduce the convergence error. We apply KM to multilayer networks and propose the PILOT methodology for modeling connectivity services in a sandbox domain. PILOT uses active probes deployed in Central Offices (COs) to obtain real measurements that are used to tune a simulation scenario reproducing the real deployment with high accuracy. A simulator is eventually used to generate large amounts of realistic synthetic data for ML training and validation. We apply KM process also to a more complex network system that consists of several domains, where intra-domain controllers assist a broker plane in estimating accurate inter-domain delay. In addition, the broker identifies and corrects intra-domain model inaccuracies, as well as it computes an accurate compound model. Such models can be used for quality of service (QoS) and accurate end-to-end delay estimations. Finally, we investigate the application on KM in the context of Intent-based Networking (IBN). Knowledge in terms of traffic model and/or traffic perturbation is transferred among agents in a hierarchical architecture. This architecture can support autonomous network operation, like capacity management.La automatización de la red se ha concebido desde hace mucho tiempo. De hecho, la red de gestión de telecomunicaciones (TMN), definida por la Unión Internacional de Telecomunicaciones (ITU), es una jerarquía de capas de gestión (elemento de red, red, servicio y gestión de negocio), donde los objetivos operativos de alto nivel se propagan desde las capas superiores a las inferiores. La arquitectura de gestión de red ha evolucionado con el desarrollo del concepto de redes definidas por software (SDN) que brinda capacidad de programación para simplificar la configuración (descompone la abstracción de servicios de alto nivel en abstracciones de dispositivos de nivel inferior), organiza la operación y reacciona automáticamente a los cambios o eventos. Además, el desarrollo y despliegue de soluciones basadas en inteligencia artificial (IA) y aprendizaje automático (ML) para la toma de decisiones (bucle de control) en base a los datos de monitorización recopilados permite la automatización de la red, que tiene como objetivo reducir costes operativos. AI/ML generalmente requieren un gran conjunto de datos para entrenamiento, los cuales son difíciles de obtener. La falta de datos se puede compensar con un enfoque de autoaprendizaje colectivo. En esta tesis, vamos más allá del bucle de control tradicional antes mencionado para lograr un proceso eficiente de gestión del conocimiento (KM) que mejora la inteligencia de la red al tiempo que reduce la complejidad. En esta tesis doctoral, proponemos una arquitectura general para apoyar el proceso de KM basada en cuatro pilares principales que permiten crear, compartir, asimilar y utilizar el conocimiento. A continuación, se proponen dos estrategias alternativas basadas en inexactitudes del modelo y modelo de combinación. Para resaltar la capacidad de KM para adaptarse a diferentes aplicaciones, se consideran dos casos de uso para implementar KM en una arquitectura de red óptica puramente centralizada y distribuida. Junto a ellos, se consideran diversas políticas para evaluar KM en estrategias basadas en datos y modelos. Los resultados apuntan a minimizar la cantidad de datos que deben compartirse y reducir el error de convergencia. Aplicamos KM a redes multicapa y proponemos la metodología PILOT para modelar servicios de conectividad en un entorno aislado. PILOT utiliza sondas activas desplegadas en centrales de telecomunicación (CO) para obtener medidas reales que se utilizan para ajustar un escenario de simulación que reproducen un despliegue real con alta precisión. Un simulador se utiliza finalmente para generar grandes cantidades de datos sintéticos realistas para el entrenamiento y la validación de ML. Aplicamos el proceso de KM también a un sistema de red más complejo que consta de varios dominios, donde los controladores intra-dominio ayudan a un plano de bróker a estimar el retardo entre dominios de forma precisa. Además, el bróker identifica y corrige las inexactitudes de los modelos intra-dominio, así como también calcula un modelo compuesto preciso. Estos modelos se pueden utilizar para estimar la calidad de servicio (QoS) y el retardo extremo a extremo de forma precisa. Finalmente, investigamos la aplicación en KM en el contexto de red basada en intención (IBN). El conocimiento en términos de modelo de tráfico y/o perturbación del tráfico se transfiere entre agentes en una arquitectura jerárquica. Esta arquitectura puede soportar el funcionamiento autónomo de la red, como la gestión de la capacidad.Postprint (published version