Distributed control in virtualized networks

The increasing number of the Internet connected devices requires novel solutions to control the next generation network resources. The cooperation between the Software Defined Network (SDN) and the Network Function Virtualization (NFV) seems to be a promising technology paradigm. The bottleneck of current SDN/NFV implementations is the use of a centralized controller. In this paper, different scenarios to identify the pro and cons of a distributed control-plane were investigated. We implemented a prototypal framework to benchmark different centralized and distributed approaches. The test results have been critically analyzed and related considerations and recommendations have been reported. The outcome of our research influenced the control plane design of the following European R&D projects: PLATINO, FI-WARE and T-NOVA

Towards edge robotics: the progress from cloud-based robotic systems to intelligent and context-aware robotic services

Current robotic systems handle a different range of applications such as video surveillance, delivery of goods, cleaning, material handling, assembly, painting, or pick and place services. These systems have been embraced not only by the general population but also by the vertical industries to help them in performing daily activities. Traditionally, the robotic systems have been deployed in standalone robots that were exclusively dedicated to performing a specific task such as cleaning the floor in indoor environments. In recent years, cloud providers started to offer their infrastructures to robotic systems for offloading some of the robot’s functions. This ultimate form of the distributed robotic system was first introduced 10 years ago as cloud robotics and nowadays a lot of robotic solutions are appearing in this form. As a result, standalone robots became software-enhanced objects with increased reconfigurability as well as decreased complexity and cost. Moreover, by offloading the heavy processing from the robot to the cloud, it is easier to share services and information from various robots or agents to achieve better cooperation and coordination. Cloud robotics is suitable for human-scale responsive and delay-tolerant robotic functionalities (e.g., monitoring, predictive maintenance). However, there is a whole set of real-time robotic applications (e.g., remote control, motion planning, autonomous navigation) that can not be executed with cloud robotics solutions, mainly because cloud facilities traditionally reside far away from the robots. While the cloud providers can ensure certain performance in their infrastructure, very little can be ensured in the network between the robots and the cloud, especially in the last hop where wireless radio access networks are involved. Over the last years advances in edge computing, fog computing, 5G NR, network slicing, Network Function Virtualization (NFV), and network orchestration are stimulating the interest of the industrial sector to satisfy the stringent and real-time requirements of their applications. Robotic systems are a key piece in the industrial digital transformation and their benefits are very well studied in the literature. However, designing and implementing a robotic system that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency, reliability) is an ambitious task. This thesis studies the integration of modern Information andCommunication Technologies (ICTs) in robotic systems and proposes some robotic enhancements that tackle the real-time constraints of robotic services. To evaluate the performance of the proposed enhancements, this thesis departs from the design and prototype implementation of an edge native robotic system that embodies the concepts of edge computing, fog computing, orchestration, and virtualization. The proposed edge robotics system serves to represent two exemplary robotic applications. In particular, autonomous navigation of mobile robots and remote-control of robot manipulator where the end-to-end robotic system is distributed between the robots and the edge server. The open-source prototype implementation of the designed edge native robotic system resulted in the creation of two real-world testbeds that are used in this thesis as a baseline scenario for the evaluation of new innovative solutions in robotic systems. After detailing the design and prototype implementation of the end-to-end edge native robotic system, this thesis proposes several enhancements that can be offered to robotic systems by adapting the concept of edge computing via the Multi-Access Edge Computing (MEC) framework. First, it proposes exemplary network context-aware enhancements in which the real-time information about robot connectivity and location can be used to dynamically adapt the end-to-end system behavior to the actual status of the communication (e.g., radio channel). Three different exemplary context-aware enhancements are proposed that aim to optimize the end-to-end edge native robotic system. Later, the thesis studies the capability of the edge native robotic system to offer potential savings by means of computation offloading for robot manipulators in different deployment configurations. Further, the impact of different wireless channels (e.g., 5G, 4G andWi-Fi) to support the data exchange between a robot manipulator and its remote controller are assessed. In the following part of the thesis, the focus is set on how orchestration solutions can support mobile robot systems to make high quality decisions. The application of OKpi as an orchestration algorithm and DLT-based federation are studied to meet the KPIs that autonomously controlledmobile robots have in order to provide uninterrupted connectivity over the radio access network. The elaborated solutions present high compatibility with the designed edge robotics system where the robot driving range is extended without any interruption of the end-to-end edge robotics service. While the DLT-based federation extends the robot driving range by deploying access point extension on top of external domain infrastructure, OKpi selects the most suitable access point and computing resource in the cloud-to-thing continuum in order to fulfill the latency requirements of autonomously controlled mobile robots. To conclude the thesis the focus is set on how robotic systems can improve their performance by leveraging Artificial Intelligence (AI) and Machine Learning (ML) algorithms to generate smart decisions. To do so, the edge native robotic system is presented as a true embodiment of a Cyber-Physical System (CPS) in Industry 4.0, showing the mission of AI in such concept. It presents the key enabling technologies of the edge robotic system such as edge, fog, and 5G, where the physical processes are integrated with computing and network domains. The role of AI in each technology domain is identified by analyzing a set of AI agents at the application and infrastructure level. In the last part of the thesis, the movement prediction is selected to study the feasibility of applying a forecast-based recovery mechanism for real-time remote control of robotic manipulators (FoReCo) that uses ML to infer lost commands caused by interference in the wireless channel. The obtained results are showcasing the its potential in simulation and real-world experimentation.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Karl Holger.- Secretario: Joerg Widmer.- Vocal: Claudio Cicconett

Resource Management From Single-domain 5G to End-to-End 6G Network Slicing:A Survey

Network Slicing (NS) is one of the pillars of the fifth/sixth generation (5G/6G) of mobile networks. It provides the means for Mobile Network Operators (MNOs) to leverage physical infrastructure across different technological domains to support different applications. This survey analyzes the progress made on NS resource management across these domains, with a focus on the interdependence between domains and unique issues that arise in cross-domain and End-to-End (E2E) settings. Based on a generic problem formulation, NS resource management functionalities (e.g., resource allocation and orchestration) are examined across domains, revealing their limits when applied separately per domain. The appropriateness of different problem-solving methodologies is critically analyzed, and practical insights are provided, explaining how resource management should be rethought in cross-domain and E2E contexts. Furthermore, the latest advancements are reported through a detailed analysis of the most relevant research projects and experimental testbeds. Finally, the core issues facing NS resource management are dissected, and the most pertinent research directions are identified, providing practical guidelines for new researchers.<br/

Machine Learning-Powered Management Architectures for Edge Services in 5G Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

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

Resource Requirements of an Edge-based Digital Twin Service: An Experimental Study

Digital Twin (DT) is a pivotal application under the industrial digital transformation envisaged by the fourth industrial revolution (Industry 4.0). DT defines intelligent and real-time faithful reflections of physical entities such as industrial robots, thus allowing their remote control. Relying on the latest advances in Information and Communication Technologies (ICT), namely Network Function Virtualization (NFV) and Edge-computing, DT can be deployed as an on-demand service in the factories close proximity and offered leveraging radio access technologies. However, with the purpose of achieving the well-known scalability, flexibility, availability and performance guarantees benefits foreseen by the latest ICT, it is steadily required to experimentally profile and assess DT as a Service (DTaaS) solutions. Moreover, the dependencies between the resources claimed by the service and the relative demand and work loads require to be investigated. In this work, an Edge-based Digital Twin solution for remote control of robotic arms is deployed in an experimental testbed where, in compliance with the NFV paradigm, the service has been segmented in virtual network functions. Our research has primarily the objective to evaluate the entanglement among overall service performance and VNFs resource requirements, and the number of robots consuming the service varies. Experimental profiles show the most critical DT features to be the inverse kinematics and trajectory computations. Moreover, the same analysis has been carried out as a function of the industrial processes, namely based on the commands imposed on the robots, and particularly of their abstraction-level, resulting in a novel trade-off between computing and time resources requirements and trajectory guarantees. The derived results provide crucial insights for the design of network service scaling and resource orchestration frameworks dealing with DTaaS applications. Finally, we empirically prove LTE shortage to accommodate the minimum DT latency requirements

A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordNetwork slicing has been widely agreed as a promising technique to accommodate diverse services for the Industrial Internet of Things (IIoT). Smart transportation, smart energy, and smart factory/manufacturing are the three key services to form the backbone of IIoT. Network slicing management is of paramount importance in the face of IIoT services with diversified requirements. It is important to have a comprehensive survey on intelligent network slicing management to provide guidance for future research in this field. In this paper, we provide a thorough investigation and analysis of network slicing management in its general use cases as well as specific IIoT services including smart transportation, smart energy and smart factory, and highlight the advantages and drawbacks across many existing works/surveys and this current survey in terms of a set of important criteria. In addition, we present an architecture for intelligent network slicing management for IIoT focusing on the above three IIoT services. For each service, we provide a detailed analysis of the application requirements and network slicing architecture, as well as the associated enabling technologies. Further, we present a deep understanding of network slicing orchestration and management for each service, in terms of orchestration architecture, AI-assisted management and operation, edge computing empowered network slicing, reliability, and security. For the presented architecture for intelligent network slicing management and its application in each IIoT service, we identify the corresponding key challenges and open issues that can guide future research. To facilitate the understanding of the implementation, we provide a case study of the intelligent network slicing management for integrated smart transportation, smart energy, and smart factory. Some lessons learnt include: 1) For smart transportation, it is necessary to explicitly identify service function chains (SFCs) for specific applications along with the orchestration of underlying VNFs/PNFs for supporting such SFCs; 2) For smart energy, it is crucial to guarantee both ultra-low latency and extremely high reliability; 3) For smart factory, resource management across heterogeneous network domains is of paramount importance. We hope that this survey is useful for both researchers and engineers on the innovation and deployment of intelligent network slicing management for IIoT.Engineering and Physical Sciences Research Council (EPSRC)Singapore University of Technology and Design (SUTD)Hong Kong RGC Research Impact Fund (RIF)National Natural Science Foundation of ChinaShenzhen Science and Technology Innovation Commissio

ML-driven provisioning and management of vertical services in automated cellular networks

One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.This work was supported by the EU Commission through the 5GROWTH project (Grant Agreement No. 856709), Spanish MINECO 5G-REFINE project (TEC2017-88373-R), and Generalitat de Catalunya 2017 SGR 1195.Publicad

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing