Securing Communication in MANET through E-GAMAN Algorithm

MANET consists of mobile nodes that are in radio reach of one. Each of the nodes has a wireless interface to correspond with one another. All networking functions, for example, routing and data transmission, are performed by nodes themselves in an organizing toward oneself way. Because of these reasons, securing communication in MANET is extremely difficult. In this study, we proposed an improved QoS routing algorithm for MANETs called E-GAMAN. The proposed methodology has two algorithms: SSRA and GAMAN. Simulation results show that E-GAMAN algorithm has a great execution and is a guaranteeing algorithm for QoS routing in MANET

Multipath routing for video delivery over bandwidth-limited networks

The delivery of quality video service often requires high bandwidth with low delay or cost in network transmission. Current routing protocols such as those used in the Internet are mainly based on the single-path approach (e.g., the shortest-path routing). This approach cannot meet the end-to-end bandwidth requirement when the video is streamed over bandwidth-limited networks. In order to overcome this limitation, we propose multipath routing, where the video takes multiple paths to reach its destination(s), thereby increasing the aggregate throughput. We consider both unicast (point-to-point) and multicast scenarios. For unicast, we present an efficient multipath heuristic (of complexity O(|V|3)), which achieves high bandwidth with low delay. Given a set of path lengths, we then present and prove a simple data scheduling algorithm as implemented at the server, which achieves the theoretical minimum end-to-end delay. For a network with unit-capacity links, the algorithm, when combined with disjoint-path routing, offers an exact and efficient solution to meet a bandwidth requirement with minimum delay. For multicast, we study the construction of multiple trees for layered video to satisfy the user bandwidth requirements. We propose two efficient heuristics on how such trees can be constructed so as to minimize the cost of their aggregation subject to a delay constraint.published_or_final_versio

Towards a cloud enabler : from an optical network resource provisioning system to a generalized architecture for dynamic infrastructure services provisioning

This work was developed during a period where most of the optical management and provisioning system where manual and proprietary. This work contributed to the evolution of the state of the art of optical networks with new architectures and advanced virtual infrastructure services. The evolution of optical networks, and internet globally, have been very promising during the last decade. The impact of mobile technology, grid, cloud computing, HDTV, augmented reality and big data, among many others, have driven the evolution of optical networks towards current service technologies, mostly based on SDN (Software Defined Networking) architectures and NFV(Network Functions Virtualisation). Moreover, the convergence of IP/Optical networks and IT services, and the evolution of the internet and optical infrastructures, have generated novel service orchestrators and open source frameworks. In fact, technology has evolved that fast that none could foresee how important Internet is for our current lives. Said in other words, technology was forced to evolve in a way that network architectures became much more transparent, dynamic and flexible to the end users (applications, user interfaces or simple APIs). This Thesis exposes the work done on defining new architectures for Service Oriented Networks and the contribution to the state of the art. The research work is divided into three topics. It describes the evolution from a Network Resource Provisioning System to an advanced Service Plane, and ends with a new architecture that virtualized the optical infrastructure in order to provide coordinated, on-demand and dynamic services between the application and the network infrastructure layer, becoming an enabler for the new generation of cloud network infrastructures. The work done on defining a Network Resource Provisioning System established the first bases for future work on network infrastructure virtualization. The UCLP (User Light Path Provisioning) technology was the first attempt for Customer Empowered Networks and Articulated Private Networks. It empowered the users and brought virtualization and partitioning functionalities into the optical data plane, with new interfaces for dynamic service provisioning. The work done within the development of a new Service Plane allowed the provisioning of on-demand connectivity services from the application, and in a multi-domain and multi-technology scenario based on a virtual network infrastructure composed of resources from different infrastructure providers. This Service Plane facilitated the deployment of applications consuming large amounts of data under deterministic conditions, so allowing the networks behave as a Grid-class resource. It became the first on-demand provisioning system that at lower levels allowed the creation of one virtual domain composed from resources of different providers. The last research topic presents an architecture that consolidated the work done in virtualisation while enhancing the capabilities to upper layers, so fully integrating the optical network infrastructure into the cloud environment, and so providing an architecture that enabled cloud services by integrating the request of optical network and IT infrastructure services together at the same level. It set up a new trend into the research community and evolved towards the technology we use today based on SDN and NFV. Summing up, the work presented is focused on the provisioning of virtual infrastructures from the architectural point of view of optical networks and IT infrastructures, together with the design and definition of novel service layers. It means, architectures that enabled the creation of virtual infrastructures composed of optical networks and IT resources, isolated and provisioned on-demand and in advance with infrastructure re-planning functionalities, and a new set of interfaces to open up those services to applications or third parties.Aquesta tesi es va desenvolupar durant un període on la majoria de sistemes de gestió de xarxa òptica eren manuals i basats en sistemes propietaris. En aquest sentit, la feina presentada va contribuir a l'evolució de l'estat de l'art de les xarxes òptiques tant a nivell d’arquitectures com de provisió d’infraestructures virtuals. L'evolució de les xarxes òptiques, i d'Internet a nivell mundial, han estat molt prometedores durant l'última dècada. L'impacte de la tecnologia mòbil, la computació al núvol, la televisió d'alta definició, la realitat augmentada i el big data, entre molts altres, han impulsat l'evolució cap a xarxes d’altes prestacions amb nous serveis basats en SDN (Software Defined Networking) i NFV (Funcions de xarxa La virtualització). D'altra banda, la convergència de xarxes òptiques i els serveis IT, junt amb l'evolució d'Internet i de les infraestructures òptiques, han generat nous orquestradors de serveis i frameworks basats en codi obert. La tecnologia ha evolucionat a una velocitat on ningú podria haver predit la importància que Internet està tenint en el nostre dia a dia. Dit en altres paraules, la tecnologia es va veure obligada a evolucionar d'una manera on les arquitectures de xarxa es fessin més transparent, dinàmiques i flexibles vers als usuaris finals (aplicacions, interfícies d'usuari o APIs simples). Aquesta Tesi presenta noves arquitectures de xarxa òptica orientades a serveis. El treball de recerca es divideix en tres temes. Es presenta un sistema de virtualització i aprovisionament de recursos de xarxa i la seva evolució a un pla de servei avançat, per acabar presentant el disseny d’una nova arquitectura capaç de virtualitzar la infraestructura òptica i IT i proporcionar serveis de forma coordinada, i sota demanda, entre l'aplicació i la capa d'infraestructura de xarxa òptica. Tot esdevenint un facilitador per a la nova generació d'infraestructures de xarxa en el núvol. El treball realitzat en la definició del sistema de virtualització de recursos va establir les primeres bases sobre la virtualització de la infraestructura de xarxa òptica en el marc de les “Customer Empowered Networks” i “Articulated Private Networks”. Amb l’objectiu de virtualitzar el pla de dades òptic, i oferir noves interfícies per a la provisió de serveis dinàmics de xarxa. En quant al pla de serveis presentat, aquest va facilitat la provisió de serveis de connectivitat sota demanda per part de l'aplicació, tant en entorns multi-domini, com en entorns amb múltiples tecnologies. Aquest pla de servei, anomenat Harmony, va facilitar el desplegament de noves aplicacions que consumien grans quantitats de dades en condicions deterministes. En aquest sentit, va permetre que les xarxes es comportessin com un recurs Grid, i per tant, va esdevenir el primer sistema d'aprovisionament sota demanda que permetia la creació de dominis virtuals de xarxa composts a partir de recursos de diferents proveïdors. Finalment, es presenta l’evolució d’un pla de servei cap una arquitectura global que consolida el treball realitzat a nivell de convergència d’infraestructures (òptica + IT) i millora les capacitats de les capes superiors. Aquesta arquitectura va facilitar la plena integració de la infraestructura de xarxa òptica a l'entorn del núvol. En aquest sentit, aquest resultats van evolucionar cap a les tendències actuals de SDN i NFV. En resum, el treball presentat es centra en la provisió d'infraestructures virtuals des del punt de vista d’arquitectures de xarxa òptiques i les infraestructures IT, juntament amb el disseny i definició de nous serveis de xarxa avançats, tal i com ho va ser el servei de re-planificació dinàmicaPostprint (published version

A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization

Many real-world problems can be formulated as numerical optimization with certain objective functions. However, these objective functions often contain numerous local optima, which could trap an algorithm from moving toward the desired global solution. To improve the search efficiency of traditional genetic algorithms, this paper presents a mutual-evaluation genetic algorithm (MEGA). A novel mutual-evaluation approach is employed so that the merit of selected genes in a chromosome can be determined by comparing the fitness changes before and after interchanging with those in the mating chromosome. According to the determined genome merit, a therapy crossover can generate effective schemata to explore the solution space efficiently. The computational experiments for twelve numerical problems show that the MEGA can find near optimal solutions in all test benchmarks and achieve solutions with higher accuracy than those obtained by eight existing algorithms. This study also uses the MEGA to find optimal flow-allocation strategies for multipath-routing problems. Experiments on quality-of-service routing scenarios show that the MEGA can deal with these constrained routing problems effectively and efficiently. Therefore, the MEGA not only can reduce the effort of function analysis but also can deal with a wide spectrum of real-world problems

A Survey on Energy Efficient Routing Protocols in Wireless Sensor Networks

Energy efficiency is one of the critical issues in the Wireless Sensor Networks (WSNs), since sensor devices are tiny and integrated with a limited capacity battery. In most of the advanced applications, WSNs operate in very harsh areas and not under supervision of human controls. Routing protocols play a significant role in energy balancing by incorporating the techniques that can reduce control overhead, proper data aggregation method and feasible path selection. It demands a unique requirement due to its frequent topology changes and distributive nature. One of the major concerns in the design of routing protocol in WSNs is efficient energy usage and prolonging Network lifetime. This paper mainly discusses different issues related to energy efficiency in routing protocols of all categories. It incorporates most recent routing protocols which improves the energy efficiency in various application environments. This paper also provides comprehensive details of each protocol which emphasize their principles and explore their advantages and limitations. These protocols belong to different classifications based on Network Structures, communication model, topology and QoS parameters. It also includes more relevant and prominent comparisons with all recent State-of-Art works

Enabling knowledge-defined networks : deep reinforcement learning, graph neural networks and network analytics

Significant breakthroughs in the last decade in the Machine Learning (ML) field have ushered in a new era of Artificial Intelligence (AI). Particularly, recent advances in Deep Learning (DL) have enabled to develop a new breed of modeling and optimization tools with a plethora of applications in different fields like natural language processing, or computer vision. In this context, the Knowledge-Defined Networking (KDN) paradigm highlights the lack of adoption of AI techniques in computer networks and – as a result – proposes a novel architecture that relies on Software-Defined Networking (SDN) and modern network analytics techniques to facilitate the deployment of ML-based solutions for efficient network operation. This dissertation aims to be a step forward in the realization of Knowledge-Defined Networks. In particular, we focus on the application of AI techniques to control and optimize networks more efficiently and automatically. To this end, we identify two components within the KDN context whose development may be crucial to achieve self-operating networks in the future: (i) the automatic control module, and (ii) the network analytics platform. The first part of this thesis is devoted to the construction of efficient automatic control modules. First, we explore the application of Deep Reinforcement Learning (DRL) algorithms to optimize the routing configuration in networks. DRL has recently demonstrated an outstanding capability to solve efficiently decision-making problems in other fields. However, first DRL-based attempts to optimize routing in networks have failed to achieve good results, often under-performing traditional heuristics. In contrast to previous DRL-based solutions, we propose a more elaborate network representation that facilitates DRL agents to learn efficient routing strategies. Our evaluation results show that DRL agents using the proposed representation achieve better performance and learn faster how to route traffic in an Optical Transport Network (OTN) use case. Second, we lay the foundations on the use of Graph Neural Networks (GNN) to build ML-based network optimization tools. GNNs are a newly proposed family of DL models specifically tailored to operate and generalize over graphs of variable size and structure. In this thesis, we posit that GNNs are well suited to model the relationships between different network elements inherently represented as graphs (e.g., topology, routing). Particularly, we use a custom GNN architecture to build a routing optimization solution that – unlike previous ML-based proposals – is able to generalize well to topologies, routing configurations, and traffic never seen during the training phase. The second part of this thesis investigates the design of practical and efficient network analytics solutions in the KDN context. Network analytics tools are crucial to provide the control plane with a rich and timely view of the network state. However this is not a trivial task considering that all this information turns typically into big data in real-world networks. In this context, we analyze the main aspects that should be considered when measuring and classifying traffic in SDN (e.g., scalability, accuracy, cost). As a result, we propose a practical solution that produces flow-level measurement reports similar to those of NetFlow/IPFIX in traditional networks. The proposed system relies only on native features of OpenFlow – currently among the most established standards in SDN – and incorporates mechanisms to maintain efficiently flow-level statistics in commodity switches and report them asynchronously to the control plane. Additionally, a system that combines ML and Deep Packet Inspection (DPI) identifies the applications that generate each traffic flow.La evolución del campo del Aprendizaje Maquina (ML) en la última década ha dado lugar a una nueva era de la Inteligencia Artificial (AI). En concreto, algunos avances en el campo del Aprendizaje Profundo (DL) han permitido desarrollar nuevas herramientas de modelado y optimización con múltiples aplicaciones en campos como el procesado de lenguaje natural, o la visión artificial. En este contexto, el paradigma de Redes Definidas por Conocimiento (KDN) destaca la falta de adopción de técnicas de AI en redes y, como resultado, propone una nueva arquitectura basada en Redes Definidas por Software (SDN) y en técnicas modernas de análisis de red para facilitar el despliegue de soluciones basadas en ML. Esta tesis pretende representar un avance en la realización de redes basadas en KDN. En particular, investiga la aplicación de técnicas de AI para operar las redes de forma más eficiente y automática. Para ello, identificamos dos componentes en el contexto de KDN cuyo desarrollo puede resultar esencial para conseguir redes operadas autónomamente en el futuro: (i) el módulo de control automático y (ii) la plataforma de análisis de red. La primera parte de esta tesis aborda la construcción del módulo de control automático. En primer lugar, se explora el uso de algoritmos de Aprendizaje Profundo por Refuerzo (DRL) para optimizar el encaminamiento de tráfico en redes. DRL ha demostrado una capacidad sobresaliente para resolver problemas de toma de decisiones en otros campos. Sin embargo, los primeros trabajos que han aplicado DRL a la optimización del encaminamiento en redes no han conseguido rendimientos satisfactorios. Frente a dichas soluciones previas, proponemos una representación más elaborada de la red que facilita a los agentes DRL aprender estrategias de encaminamiento eficientes. Nuestra evaluación muestra que cuando los agentes DRL utilizan la representación propuesta logran mayor rendimiento y aprenden más rápido cómo encaminar el tráfico en un caso práctico en Redes de Transporte Ópticas (OTN). En segundo lugar, se presentan las bases sobre la utilización de Redes Neuronales de Grafos (GNN) para construir herramientas de optimización de red. Las GNN constituyen una nueva familia de modelos de DL específicamente diseñados para operar y generalizar sobre grafos de tamaño y estructura variables. Esta tesis destaca la idoneidad de las GNN para modelar las relaciones entre diferentes elementos de red que se representan intrínsecamente como grafos (p. ej., topología, encaminamiento). En particular, utilizamos una arquitectura GNN específicamente diseñada para optimizar el encaminamiento de tráfico que, a diferencia de las propuestas anteriores basadas en ML, es capaz de generalizar correctamente sobre topologías, configuraciones de encaminamiento y tráfico nunca vistos durante el entrenamiento La segunda parte de esta tesis investiga el diseño de herramientas de análisis de red eficientes en el contexto de KDN. El análisis de red resulta esencial para proporcionar al plano de control una visión completa y actualizada del estado de la red. No obstante, esto no es una tarea trivial considerando que esta información representa una cantidad masiva de datos en despliegues de red reales. Esta parte de la tesis analiza los principales aspectos a considerar a la hora de medir y clasificar el tráfico en SDN (p. ej., escalabilidad, exactitud, coste). Como resultado, se propone una solución práctica que genera informes de medidas de tráfico a nivel de flujo similares a los de NetFlow/IPFIX en redes tradicionales. El sistema propuesto utiliza sólo funciones soportadas por OpenFlow, actualmente uno de los estándares más consolidados en SDN, y permite mantener de forma eficiente estadísticas de tráfico en conmutadores con características básicas y enviarlas de forma asíncrona hacia el plano de control. Asimismo, un sistema que combina ML e Inspección Profunda de Paquetes (DPI) identifica las aplicaciones que generan cada flujo de tráfico.Postprint (published version

Variable optical true-time delay line breaking bandwidth-delay constraints

Continuously variable true-time optical delay lines are typically subject to a constraint of the bandwidth-delay product, limiting their use in several applications. In this Letter, we propose an integrated topology that breaks the bandwidth-delay product limit. The device is based on multiple Mach-Zehnder Interferometers (MZIs) arranged in parallel, providing easier control and a larger bandwidth compared to ring resonator-based solutions. The functionality of this architecture is demonstrated with a 4-stage delay line by performing measurements in both the time and frequency domains. The delay line introduces a delay of 90 ps over a bandwidth of more than 22 GHz with a negligible group delay distortion, operates on a wavelength range of about 60 nm, and is scalable to a higher number of MZI stages