Software-Defined Networks for Future Networks and Services: Main Technical Challenges and Business Implications

In 2013, the IEEE Future Directions Committee (FDC) formed an SDN work group to explore the amount of interest in forming an IEEE Software-Defined Network (SDN) Community. To this end, a Workshop on "SDN for Future Networks and Services" (SDN4FNS'13) was organized in Trento, Italy (Nov. 11th-13th 2013). Following the results of the workshop, in this paper, we have further analyzed scenarios, prior-art, state of standardization, and further discussed the main technical challenges and socio-economic aspects of SDN and virtualization in future networks and services. A number of research and development directions have been identified in this white paper, along with a comprehensive analysis of the technical feasibility and business availability of those fundamental technologies. A radical industry transition towards the "economy of information through softwarization" is expected in the near future

Integration of SDN frameworks and Cloud Computing platforms: an Open Source approach

As a result of the explosion in the number of services offered over the Internet, network traffic has experienced a remarkable increment and is supposed to increase even more in the few next years. Therefore, Telco operators are investigating new solutions aiming at managing this traffic efficiently and transparently to guarantee the users the needed Quality of Service. The most viable solution is to have a paradigm shift in the networking field: the old and legacy routing will be indeed replaced by something more dynamic, through the use of Software Defined Networking. In addition to it, Network Functions Virtualization will play a key role making possible to virtualize the intermediate nodes implementing network functions, also called middle-boxes, on general purpose hardware. The most suitable environment to understand their potentiality is the Cloud, where resources, as computational power, storage, development platforms, etc. are outsourced and provided to the user as a service on a pay-per-use model. All of this is done in a complete dynamic way, as a result of the presence of the implementation of the above cited paradigms. However, whenever it comes to strict requirements, Telecommunication Networks are still underperforming: one of the cause is the weak integration among these paradigms to reactively intervene to the users' need. It is therefore remarkably important to properly evaluate solutions where SDN and NFV are cooperating actively inside the Cloud, leading to more adaptive systems. In this document, after the description of the state of the art in networking, the deployment of an OpenStack Cloud platform on an outperforming cluster will be shown. In addition, its networking capabilities will be improved via a careful cloud firewalling configuration; moreover, this cluster will be integrated with Open Source SDN frameworks to enhance its services. Finally, some measurements showing how much this approach could be interesting will be provided

Setup automatizzato di un cloud privato con OpenStack

Oggigiorno, l'utilizzo di un cloud sta diventando sempre più frequente in molte aziende, indipendentemente dal settore di appartenenza. Spesso, queste aziende devono affidarsi a soluzioni di cloud computing private, affrontando costi non indifferenti. OpenStack è una piattaforma di cloud privati open source che permette di ridurre questi costi senza compromettere la qualità dei servizi cloud. Inoltre, con l’ausilio di alcuni strumenti e sistemi, è possibile gestire tutte le fasi di installazione, manutenzione e gestione in modo automatizzato, rendendo facilmente replicabili tutti i processi. L'obiettivo di questa tesi è riprodurre in scala ridotta un'infrastruttura cloud privata con hardware non recente o a basso costo utilizzando dei sistemi che facilitano e implementano un livello di automatizzazione per le fasi di installazione e di uso. Per fare ciò, una volta raggruppato l’hardware, si è utilizzato il sistema MAAS per la gestione della parte fisica del cloud. MAAS ha consentito il provisioning dell’intero cluster in modo rapido e automatizzata. In seguito, utilizzando Juju, che a sua volta collabora con MAAS, è stato possibile gestire la parte software del cloud, dall’installazione del sistema operativo fino al dispiegamento di tutte quelle applicazioni che compongono il cloud OpenStack. Anche Juju ha permesso l’esecuzione di queste fasi in modo automatizzato. Una volta creato il cloud, si è utilizzato lo strumento Terraform per automatizzare i processi di inizializzazione, configurazione e istanziazione delle risorse del cloud, sia dal punto di vista dell’amministratore di sistema, sia da parte degli utenti. In conclusione, si può affermare che gli obiettivi prefissati sono stati raggiunti, riuscendo a creare un cloud privato facilmente replicabile utilizzando hardware economico; il tutto ottenendo un sistema con performance e caratteristiche adeguate

A situational awareness model for data analysis on 5G mobile networks : the SELFNET analyzer framework

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 14-07-2017Se espera que las redes 5G provean un entorno seguro, con able y de alto rendimiento con interrupciones m nimas en la provisi on de servicios avanzados de red, sin importar la localizaci on del dispositivo o cuando el servicio es requerido. Esta nueva generaci on de red ser a capaz de proporcionar altas velocidades, baja latencia y mejor Calidad de Servicio (QoS) comparado con las redes actuales Long Term Evolution (LTE). Para proveer estas capacidades, 5G propone la combinaci on de tecnolog as avanzadas tales como Redes De nidas por Software (SDN), Virtualizaci on de las Funciones de Red (NFV), Redes auto-organizadas (SON) e Inteligencia Arti cial. De manera especial, 5G ser a capaz de solucionar o mitigar cambios inesperados o problemas t picos de red a trav es de la identi caci on de situaciones espec cas, tomando en cuenta las necesidades del usuario y los Acuerdos de Nivel de Servicio (SLAs). Actualmente, los principales operadores de red y la comunidad cient ca se encuentran trabajando en estrategias para facilitar el an alisis de datos y el proceso de toma de decisiones cuando eventos espec cos comprometen la salud de las redes 5G. Al mismo tiempo, el concepto de Conciencia Situacional (SA) y los modelos de gesti on de incidencias aplicados a redes 5G est an en etapa temprana de desarrollo. La idea principal detr as de estos conceptos es prevenir o mitigar situaciones nocivas de manera reactiva y proactiva. En este contexto, el proyecto Self-Organized Network Management in Virtualized and Software De ned Networks (SELFNET) combina los conceptos de SDN, NFV and SON para proveer un marco de gesti on aut onomo e inteligente para redes 5G. SELFNET resuelve problemas comunes de red, mientras mejora la calidad de servicio (QoS) y la Calidad de Experiencia (QoE) de los usuarios nales...5G networks hope to provide a secure, reliable and high-performance environment with minimal disruptions in the provisioning of advanced network services, regardless the device location or when the service is required. This new network generation will be able to deliver ultra-high capacity, low latency and better Quality of Service (QoS) compared with current Long Term Evolution (LTE) networks. In order to provide these capabilities, 5G proposes the combination of advanced technologies such as Software De ned Networking (SDN), Network Function Virtualization (NFV), Self-organized Networks (SON) or Arti cial Intelligence. In particular, 5G will be able to face unexpected changes or network problems through the identi cation of speci c situations, taking into account the user needs and the Service Level Agreements (SLAs). Nowadays, the main telecommunication operators and community research are working in strategies to facilitate the data analysis and decision-making process when unexpected events compromise the health in 5G Networks. Meanwhile, the concept of Situational Awareness (SA) and incident management models applied to 5G Networks are also in an early stage. The key idea behind these concepts is to mitigate or prevent harmful situations in a reactive and proactive way. In this context, Self-Organized Network Management in Virtualized and Software De ned Networks Project (SELFNET) combines SDN, NFV and SON concepts to provide a smart autonomic management framework for 5G networks. SELFNET resolves common network problems, while improving the QoS and Quality of Experience (QoE) of end users...Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Implementazione cloud del piano di controllo della rete 5G

Il 5G non è esclusivamente sinonimo di connettività wireless più veloce, ma promette di trasformare i processi di consumo aziendali e industriali esistenti, guidando la prossima ondata di crescita economica globale. Per questo motivo, ci si aspetta che i nuovi paradigmi di progettazione dell'architettura di rete 5G facciano la differenza rispetto alle generazioni precedenti, introducendo importanti cambiamenti per quanto riguarda flessibilità e scalabilità. La programmabilità di rete rappresenta un elemento chiave per la progettazione, l'implementazione, la distribuzione, la gestione e la manutenzione di apparecchiature e componenti di rete mediante la programmazione del software. Ai concetti di Cloud e servizi si affiancano quelli di Software Defined Network, Network Function Virtualization e Multi-access Edge Computing. Tramite Software Defined Network, il piano di controllo è separato dal piano dati. Le funzionalità di controllo sono incapsulate in un'entità, controller SDN, logicamente separata da quella che si occupa dell'inoltro dei dati. NFV prevede di virtualizzare i componenti di rete in modo che possano essere eseguiti su hardware di comodità. NFV abilita il Network Slicing permettendo di costruire reti logiche differenti sulla stessa infrastruttura comune. Tramite MEC le capacità di calcolo e archiviazione e i servizi vengono portati il più vicino possibile all'utente: nella parte edge del Cloud. La combinazione di tutti questi paradigmi dovrebbe portare il 5G a essere una trasformazione radicale dei principali segmenti dell'economia. Il presente elaborato ha l'obiettivo di virtualizzare e orchestrare i componenti del core di rete 5G e 4G utilizzando l'approccio NFV. In particolare, lo scenario progettato prevede di mettere in esecuzione e configurare dinamicamente i componenti dei core tramite il framework ETSI NFV Management and Orchestration

Flexible cross layer optimization for fixed and mobile broadband telecommunication networks and beyond

In der heutigen Zeit, in der das Internet im Allgemeinen und Telekommunikationsnetze im Speziellen kritische Infrastrukturen erreicht haben, entstehen hohe Anforderungen und neue Herausforderungen an den Datentransport in Hinsicht auf Effizienz und Flexibilität. Heutige Telekommunikationsnetze sind jedoch rigide und statisch konzipiert, was nur ein geringes Maß an Flexibilität und Anpassungsfähigkeit der Netze ermöglicht und darüber hinaus nur im begrenzten Maße die Wichtigkeit von Datenflüssen im wiederspiegelt. Diverse Lösungsansätze zum kompletten Neuentwurf als auch zum evolutionären Konzept des Internet wurden ausgearbeitet und spezifiziert, um diese neuartigen Anforderungen und Herausforderungen adäquat zu adressieren. Einer dieser Ansätze ist das Cross Layer Optimierungs-Paradigma, welches eine bisher nicht mögliche direkte Kommunikation zwischen verteilten Funktionalitäten unterschiedlichen Typs ermöglicht, um ein höheres Maß an Dienstgüte zu erlangen. Ein wesentlicher Indikator, welcher die Relevanz dieses Ansatzes unterstreicht, zeichnet sich durch die Programmierbarkeit von Netzwerkfunktionalitäten aus, welche sich aus der Evolution von heutigen hin zu zukünftigen Netzen erkennen lässt. Dieses Konzept wird als ein vielversprechender Lösungsansatz für Kontrollmechanismen von Diensten in zukünftigen Kernnetzwerken erachtet. Dennoch existiert zur Zeit der Entstehung dieser Doktorarbeit kein Ansatz zur Cross Layer Optimierung in Festnetz-und Mobilfunknetze, welcher der geforderten Effizienz und Flexibilität gerecht wird. Die übergeordnete Zielsetzung dieser Arbeit adressiert die Konzeptionierung, Entwicklung und Evaluierung eines Cross Layer Optimierungsansatzes für Telekommunikationsnetze. Einen wesentlichen Schwerpunkt dieser Arbeit stellt die Definition einer theoretischen Konzeptionierung und deren praktischer Realisierung eines Systems zur Cross Layer Optimierung für Telekommunikationsnetze dar. Die durch diese Doktorarbeit analysierten wissenschaftlichen Fragestellungen betreffen u.a. die Anwendbarkeit von Cross Layer Optimierungsansätzen auf Telekommunikationsnetzwerke; die Betrachtung neuartiger Anforderungen; existierende Konzepte, Ansätze und Lösungen; die Abdeckung neuer Funktionalitäten durch bereits existierende Lösungen; und letztendlich den erkennbaren Mehrwert des neu vorgeschlagenen Konzepts gegenüber den bestehenden Lösungen. Die wissenschaftlichen Beiträge dieser Doktorarbeit lassen sich grob durch vier Säulen skizzieren: Erstens werden der Stand der Wissenschaft und Technik analysiert und bewertet, Anforderungen erhoben und eine Lückenanalyse vorgenommen. Zweitens werden Herausforderungen, Möglichkeiten, Limitierungen und Konzeptionierungsaspekte eines Modells zur Cross Layer Optimierung analysiert und evaluiert. Drittens wird ein konzeptionelles Modell - Generic Adaptive Resource Control (GARC) - spezifiziert, als Prototyp realisiert und ausgiebig validiert. Viertens werden theoretische und praktische Beiträge dieser Doktorarbeit vertiefend analysiert und bewertet.As the telecommunication world moves towards a data-only network environment, signaling, voice and other data are similarly transported as Internet Protocol packets. New requirements, challenges and opportunities are bound to this transition and influence telecommunication architectures accordingly. In this time in which the Internet in general, and telecommunication networks in particular, have entered critical infrastructures and systems, it is of high importance to guarantee efficient and flexible data transport. A certain level of Quality-of-Service (QoS) for critical services is crucial even during overload situations in the access and core network, as these two are the bottlenecks in the network. However, the current telecommunication architecture is rigid and static, which offers very limited flexibility and adaptability. Several concepts on clean slate as well as evolutionary approaches have been proposed and defined in order to cope with these new challenges and requirements. One of these approaches is the Cross Layer Optimization paradigm. This concept omits the strict separation and isolation of the Application-, Control- and Network-Layers as it enables interaction and fosters Cross Layer Optimization among them. One indicator underlying this trend is the programmability of network functions, which emerges clearly during the telecommunication network evolution towards the Future Internet. The concept is regarded as one solution for service control in future mobile core networks. However, no standardized approach for Cross Layer signaling nor optimizations in between the individual layers have been standardized at the time this thesis was written. The main objective of this thesis is the design, implementation and evaluation of a Cross Layer Optimization concept on telecommunication networks. A major emphasis is given to the definition of a theoretical model and its practical realization through the implementation of a Cross Layer network resource optimization system for telecommunication systems. The key questions answered through this thesis are: in which way can the Cross Layer Optimization paradigm be applied on telecommunication networks; which new requirements arise; which of the required functionalities cannot be covered through existing solutions, what other conceptual approaches already exist and finally whether such a new concept is viable. The work presented in this thesis and its contributions can be summarized in four parts: First, a review of related work, a requirement analysis and a gap analysis were performed. Second, challenges, limitations, opportunities and design aspects for specifying an optimization model between application and network layer were formulated. Third, a conceptual model - Generic Adaptive Resource Control (GARC) - was specified and its prototypical implementation was realized. Fourth, the theoretical and practical thesis contributions was validated and evaluated

Actas del XXIV Workshop de Investigadores en Ciencias de la Computación: WICC 2022

Compilación de las ponencias presentadas en el XXIV Workshop de Investigadores en Ciencias de la Computación (WICC), llevado a cabo en Mendoza en abril de 2022.Red de Universidades con Carreras en Informátic

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF