Ultra-reliable Low-latency, Energy-efficient and Computing-centric Software Data Plane for Network Softwarization

Network softwarization plays a significantly important role in the development and deployment of the latest communication system for 5G and beyond. A more flexible and intelligent network architecture can be enabled to provide support for agile network management, rapid launch of innovative network services with much reduction in Capital Expense (CAPEX) and Operating Expense (OPEX). Despite these benefits, 5G system also raises unprecedented challenges as emerging machine-to-machine and human-to-machine communication use cases require Ultra-Reliable Low Latency Communication (URLLC). According to empirical measurements performed by the author of this dissertation on a practical testbed, State of the Art (STOA) technologies and systems are not able to achieve the one millisecond end-to-end latency requirement of the 5G standard on Commercial Off-The-Shelf (COTS) servers. This dissertation performs a comprehensive introduction to three innovative approaches that can be used to improve different aspects of the current software-driven network data plane. All three approaches are carefully designed, professionally implemented and rigorously evaluated. According to the measurement results, these novel approaches put forward the research in the design and implementation of ultra-reliable low-latency, energy-efficient and computing-first software data plane for 5G communication system and beyond

Near real-time estimation of end-to-end performance in converged fixed-mobile networks

© Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The independent operation of mobile and fixed network segments is one of the main barriers that prevents improving network performance while reducing capital expenditures coming from overprovisioning. In particular, a coordinated dynamic network operation of both network segments is essential to guarantee end-to-end Key Performance Indicators (KPI), on which new network services rely on. To achieve such dynamic operation, accurate estimation of end-to-end KPIs is needed to trigger network reconfiguration before performance degrades. In this paper, we present a methodology to achieve an accurate, scalable, and predictive estimation of end-to-end KPIs with sub-second granularity near real-time in converged fixed-mobile networks. Specifically, we extend our CURSA-SQ methodology for mobile network traffic analysis, to enable converged fixed-mobile network operation. CURSA-SQ combines simulation and machine learning fueled with real network monitoring data. Numerical results validate the accuracy, robustness, and usability of the proposed CURSA-SQ methodology for converged fixed-mobile network scenarios.Peer ReviewedPostprint (author's final draft

Knowledge-defined networking : a machine learning based approach for network and traffic modeling

The research community has considered in the past the application of Machine Learning (ML) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this thesis, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of ML techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and ML, and provide use-cases that illustrate its applicability and benefits. We also present some relevant use-cases, in which ML tools can be useful. We refer to this new paradigm as Knowledge-Defined Networking (KDN). In this context, ML can be used as a network modeling technique to build models that estimate the network performance. Network modeling is a central technique to many networking functions, for instance in the field of optimization. One of the objective of this thesis is to provide an answer to the following question: Can neural networks accurately model the performance of a computer network as a function of the input traffic?. In this thesis, we focus mainly on modeling the average delay, but also on estimating the jitter and the packets lost. For this, we assume the network as a black-box that has as input a traffic matrix and as output the desired performance matrix. Then we train different regressors, including deep neural networks, and evaluate its accuracy under different fundamental network characteristics: topology, size, traffic intensity and routing. Moreover, we also study the impact of having multiple traffic flows between each pair of nodes. We also explore the use of ML techniques in other network related fields. One relevant application is traffic forecasting. Accurate forecasting enables scaling up or down the resources to efficiently accommodate the load of traffic. Such models are typically based on traditional time series ARMA or ARIMA models. We propose a new methodology that results from the combination of an ARIMA model with an ANN. The Neural Network greatly improves the ARIMA estimation by modeling complex and nonlinear dependencies, particularly for outliers. In order to train the Neural Network and to improve the outliers estimation, we use external information: weather, events, holidays, etc. The main hypothesis is that network traffic depends on the behavior of the end-users, which in turn depend on external factors. We evaluate the accuracy of our methodology using real-world data from an egress Internet link of a campus network. The analysis shows that the model works remarkably well, outperforming standard ARIMA models. Another relevant application is in the Network Function Virtualization (NFV). The NFV paradigm makes networks more flexible by using Virtual Network Functions (VNF) instead of dedicated hardware. The main advantage is the flexibility offered by these virtual elements. However, the use of virtual nodes increases the difficulty of modeling such networks. This problem may be addressed by the use of ML techniques, to model or to control such networks. As a first step, we focus on the modeling of the performance of single VNFs as a function of the input traffic. In this thesis, we demonstrate that the CPU consumption of a VNF can be estimated only as a function of the input traffic characteristics.L'aplicació de tècniques d'aprenentatge automàtic (ML) pel control i operació de xarxes informàtiques ja s'ha plantejat anteriorment per la comunitat científica. Un exemple important és "Knowledge Plane", proposat per D. Clark et al. Tot i això, aquestes propostes no s'han utilitzat ni implementat mai en aquest camp. En aquesta tesi, explorem els motius que han fet impossible l'adopció fins al present, i que ara en permeten la implementació. El principal motiu és l'adopció de dos nous paradigmes: Software-Defined Networking (SDN) i Network Analytics (NA), que permeten la utilització de tècniques d'aprenentatge automàtic en el context de control i operació de xarxes informàtiques. En aquesta tesi, es descriu aquest paradigma, que aprofita les possibilitats ofertes per SDN, per NA i per ML, i s'expliquen aplicacions en el món de la informàtica i les comunicacions on l'aplicació d'aquestes tècniques poden ser molt beneficioses. Hem anomenat a aquest paradigma Knowledge-Defined Networking (KDN). En aquest context, una de les aplicacions de ML és el modelatge de xarxes informàtiques per estimar-ne el comportament. El modelatge de xarxes és un camp de recerca important el aquest camp, i que permet, per exemple, optimitzar-ne el seu rendiment. Un dels objectius de la tesi és respondre la següent pregunta: Pot una xarxa neuronal modelar de manera acurada el comportament d'una xarxa informàtica en funció del tràfic d'entrada? Aquesta tesi es centra principalment en el modelatge del retard mig (temps entre que s'envia i es rep un paquet). També s'estudia com varia aquest retard (jitter) i el nombre de paquets perduts. Per fer-ho, s'assumeix que la xarxa és totalment desconeguda i que només es coneix la matriu de tràfic d'entrada i la matriu de rendiment com a sortida. Es fan servir diferents tècniques de ML, com ara regressors lineals i xarxes neuronals, i se n'avalua la precisió per diferents xarxes i diferents configuracions de xarxa i tràfic. Finalment, també s'estudia l'impacte de tenir múltiples fluxos entre els parells de nodes. En la tesi, també s'explora l'ús de tècniques d¿aprenentatge automàtic en altres camps relacionats amb les xarxes informàtiques. Un cas rellevant és la predicció de tràfic. Una bona estimació del tràfic permet preveure la utilització dels diversos elements de la xarxa i optimitzar-ne el seu rendiment. Les tècniques tradicionals de predicció de tràfic es basen en tècniques de sèries temporals, com ara models ARMA o ARIMA. En aquesta tesis es proposa una nova metodologia que combina un model ARIMA amb una xarxa neuronal. La xarxa neuronal millora la predicció dels valors atípics, que tenen comportament complexos i no lineals. Per fer-ho, s'incorpora a l'anàlisi l'ús d'informació externa, com ara: informació meteorològica, esdeveniments, vacances, etc. La hipòtesi principal és que el tràfic de xarxes informàtiques depèn del comportament dels usuaris finals, que a la vegada depèn de factors externs. Per això, s'avalua la precisió de la metodologia presentada fent servir dades reals d'un enllaç de sortida de la xarxa d'un campus. S'observa que el model presentat funciona bé, superant la precisió de models ARIMA estàndards. Una altra aplicació important és en el camp de Network Function Virtualization (NFV). El paradigma de NFV fa les xarxes més flexibles gràcies a l'ús de Virtual Network Functions (VNF) en lloc de dispositius específics. L'avantatge principal és la flexibilitat que ofereixen aquests elements virtuals. Per contra, l'ús de nodes virtuals augmenta la dificultat de modelar aquestes xarxes. Aquest problema es pot estudiar també mitjançant tècniques d'aprenentatge automàtic, tant per modelar com per controlar la xarxa. Com a primer pas, aquesta tesi es centra en el modelatge del comportament de VNFs treballant soles en funció del tràfic que processen. Concretament, es demostra que el consum de CPU d'una VNF es pot estimar a partir a partir de diverses característiques del tràfic d'entrada.Postprint (published version

QUALITY-OF-SERVICE PROVISIONING FOR SMART CITY APPLICATIONS USING SOFTWARE-DEFINED NETWORKING

In the current world, most cities have WiFi Access Points (AP) in every nook and corner. Hence upraising these cities to the status of a smart city is a more easily achievable task than before. Internet-of-Things (IoT) connections primarily use WiFi standards to form the veins of a smart city. Unfortunately, this vast potential of WiFi technology in the genesis of smart cities is somehow compromised due to its failure in meeting unique Quality-of-Service (QoS) demands of smart city applications. Out of the following QoS factors; transmission link bandwidth, packet transmission delay, jitter, and packet loss rate, not all applications call for the all of the factors at the same time. Since smart city is a pool of drastically unrelated services, this variable demand can actually be advantageous to optimize the network performance. This thesis work is an attempt to achieve one of those QoS demands, namely packet delivery latency. Three algorithms are developed to alleviate traffic load imbalance at APs so as to reduce packet forwarding delay. Software-Defined Networking (SDN) is making its way in the network world to be of great use and practicality. The algorithms make use of SDN features to control the connections to APs in order to achieve the delay requirements of smart city services. Real hardware devices are used to imitate a real-life scenario of citywide coverage consisting of WiFi devices and APs that are currently available in the market with neither of those having any additional requirements such as support for specific roaming protocol, running a software agent or sending probe packets. Extensive hardware experimentation proves the efficacy of the proposed algorithms

Fluid flow queue models for fixed-mobile network evaluation

A methodology for fast and accurate end-to-end KPI, like throughput and delay, estimation is proposed based on the service-centric traffic flow analysis and the fluid flow queuing model named CURSA-SQ. Mobile network features, like shared medium and mobility, are considered defining the models to be taken into account such as the propagation models and the fluid flow scheduling model. The developed methodology provides accurate computation of these KPIs, while performing orders of magnitude faster than discrete event simulators like ns-3. Finally, this methodology combined to its capacity for performance estimation in MPLS networks enables its application for near real-time converged fixed-mobile networks operation as it is proven in three use case scenarios

Quality of service aware data dissemination in vehicular Ad Hoc networks

Des systèmes de transport intelligents (STI) seront éventuellement fournis dans un proche avenir pour la sécurité et le confort des personnes lors de leurs déplacements sur les routes. Les réseaux ad-hoc véhiculaires (VANETs) représentent l'élément clé des STI. Les VANETs sont formés par des véhicules qui communiquent entre eux et avec l'infrastructure. En effet, les véhicules pourront échanger des messages qui comprennent, par exemple, des informations sur la circulation routière, les situations d'urgence et les divertissements. En particulier, les messages d'urgence sont diffusés par des véhicules en cas d'urgence (p.ex. un accident de voiture); afin de permettre aux conducteurs de réagir à temps (p.ex., ralentir), les messages d'urgence doivent être diffusés de manière fiable dans un délai très court. Dans les VANETs, il existe plusieurs facteurs, tels que le canal à pertes, les terminaux cachés, les interférences et la bande passante limitée, qui compliquent énormément la satisfaction des exigences de fiabilité et de délai des messages d'urgence. Dans cette thèse, en guise de première contribution, nous proposons un schéma de diffusion efficace à plusieurs sauts, appelé Dynamic Partitioning Scheme (DPS), pour diffuser les messages d'urgence. DPS calcule les tailles de partitions dynamiques et le calendrier de transmission pour chaque partition; à l'intérieur de la zone arrière de l'expéditeur, les partitions sont calculées de sorte qu'en moyenne chaque partition contient au moins un seul véhicule; l'objectif est de s'assurer que seul un véhicule dans la partition la plus éloignée (de l'expéditeur) est utilisé pour diffuser le message, jusqu'au saut suivant; ceci donne lieu à un délai d'un saut plus court. DPS assure une diffusion rapide des messages d'urgence. En outre, un nouveau mécanisme d'établissement de liaison, qui utilise des tonalités occupées, est proposé pour résoudre le problème du problème de terminal caché. Dans les VANETs, la Multidiffusion, c'est-à-dire la transmission d'un message d'une source à un nombre limité de véhicules connus en tant que destinations, est très importante. Par rapport à la diffusion unique, avec Multidiffusion, la source peut simultanément prendre en charge plusieurs destinations, via une arborescence de multidiffusion, ce qui permet d'économiser de la bande passante et de réduire la congestion du réseau. Cependant, puisque les VANETs ont une topologie dynamique, le maintien de la connectivité de l'arbre de multidiffusion est un problème majeur. Comme deuxième contribution, nous proposons deux approches pour modéliser l'utilisation totale de bande passante d'une arborescence de multidiffusion: (i) la première approche considère le nombre de segments de route impliqués dans l'arbre de multidiffusion et (ii) la seconde approche considère le nombre d'intersections relais dans l'arbre de multidiffusion. Une heuristique est proposée pour chaque approche. Pour assurer la qualité de service de l'arbre de multidiffusion, des procédures efficaces sont proposées pour le suivi des destinations et la surveillance de la qualité de service des segments de route. Comme troisième contribution, nous étudions le problème de la congestion causée par le routage du trafic de données dans les VANETs. Nous proposons (1) une approche de routage basée sur l’infonuagique qui, contrairement aux approches existantes, prend en compte les chemins de routage existants qui relaient déjà les données dans les VANETs. Les nouvelles demandes de routage sont traitées de sorte qu'aucun segment de route ne soit surchargé par plusieurs chemins de routage croisés. Au lieu d'acheminer les données en utilisant des chemins de routage sur un nombre limité de segments de route, notre approche équilibre la charge des données en utilisant des chemins de routage sur l'ensemble des tronçons routiers urbains, dans le but d'empêcher, dans la mesure du possible, les congestions locales dans les VANETs; et (2) une approche basée sur le réseau défini par logiciel (SDN) pour surveiller la connectivité VANET en temps réel et les délais de transmission sur chaque segment de route. Les données de surveillance sont utilisées en entrée de l'approche de routage.Intelligent Transportation Systems (ITS) will be eventually provided in the near future for both safety and comfort of people during their travel on the roads. Vehicular ad-hoc Networks (VANETs), represent the key component of ITS. VANETs consist of vehicles that communicate with each other and with the infrastructure. Indeed, vehicles will be able to exchange messages that include, for example, information about road traffic, emergency situations, and entertainment. Particularly, emergency messages are broadcasted by vehicles in case of an emergency (e.g., car accident); in order to allow drivers to react in time (e.g., slow down), emergency messages must be reliably disseminated with very short delay. In VANETs, there are several factors, such as lossy channel, hidden terminals, interferences and scarce bandwidth, which make satisfying reliability and delay requirements of emergency messages very challenging. In this thesis, as the first contribution, we propose a reliable time-efficient and multi-hop broadcasting scheme, called Dynamic Partitioning Scheme (DPS), to disseminate emergency messages. DPS computes dynamic partition sizes and the transmission schedule for each partition; inside the back area of the sender, the partitions are computed such that in average each partition contains at least a single vehicle; the objective is to ensure that only a vehicle in the farthest partition (from the sender) is used to disseminate the message, to next hop, resulting in shorter one hop delay. DPS ensures fast dissemination of emergency messages. Moreover, a new handshaking mechanism, that uses busy tones, is proposed to solve the problem of hidden terminal problem. In VANETs, Multicasting, i.e. delivering a message from a source to a limited known number of vehicles as destinations, is very important. Compared to Unicasting, with Multicasting, the source can simultaneously support multiple destinations, via a multicast tree, saving bandwidth and reducing overall communication congestion. However, since VANETs have a dynamic topology, maintaining the connectivity of the multicast tree is a major issue. As the second contribution, we propose two approaches to model total bandwidth usage of a multicast tree: (i) the first approach considers the number of road segments involved in the multicast tree and (ii) the second approach considers the number of relaying intersections involved in the multicast tree. A heuristic is proposed for each approach. To ensure QoS of the multicasting tree, efficient procedures are proposed for tracking destinations and monitoring QoS of road segments. As the third contribution, we study the problem of network congestion in routing data traffic in VANETs. We propose (1) a Cloud-based routing approach that, in opposition to existing approaches, takes into account existing routing paths which are already relaying data in VANETs. New routing requests are processed such that no road segment gets overloaded by multiple crossing routing paths. Instead of routing over a limited set of road segments, our approach balances the load of communication paths over the whole urban road segments, with the objective to prevent, whenever possible, local congestions in VANETs; and (2) a Software Defined Networking (SDN) based approach to monitor real-time VANETs connectivity and transmission delays on each road segment. The monitoring data is used as input to the routing approach

Enabling Scalable and Sustainable Softwarized 5G Environments

The fifth generation of telecommunication systems (5G) is foreseen to play a fundamental role in our socio-economic growth by supporting various and radically new vertical applications (such as Industry 4.0, eHealth, Smart Cities/Electrical Grids, to name a few), as a one-fits-all technology that is enabled by emerging softwarization solutions \u2013 specifically, the Fog, Multi-access Edge Computing (MEC), Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) paradigms. Notwithstanding the notable potential of the aforementioned technologies, a number of open issues still need to be addressed to ensure their complete rollout. This thesis is particularly developed towards addressing the scalability and sustainability issues in softwarized 5G environments through contributions in three research axes: a) Infrastructure Modeling and Analytics, b) Network Slicing and Mobility Management, and c) Network/Services Management and Control. The main contributions include a model-based analytics approach for real-time workload profiling and estimation of network key performance indicators (KPIs) in NFV infrastructures (NFVIs), as well as a SDN-based multi-clustering approach to scale geo-distributed virtual tenant networks (VTNs) and to support seamless user/service mobility; building on these, solutions to the problems of resource consolidation, service migration, and load balancing are also developed in the context of 5G. All in all, this generally entails the adoption of Stochastic Models, Mathematical Programming, Queueing Theory, Graph Theory and Team Theory principles, in the context of Green Networking, NFV and SDN