No Interruption When Reconfiguring my SFCs

This work has been supported by the French government through the UCAJEDI (ANR-15-IDEX-01) and EUR DS4H (ANR-17-EURE-004) Investments in the Future projects, and by Inria associated team EfDyNet.International audienceSoftware Defined Networking (SDN) and NetworkFunction Virtualization (NFV) are complementary and corecomponents of modernized networks. In this paper, we considerthe problem of reconfiguring Service Function Chains (SFC)with the goal of bringing the network from a sub-optimal toan optimal operational state. We propose optimization modelsbased on themake-before-breakmechanism, in which a new pathis set up before the old one is torn down. Our method takes intoconsideration the chaining requirements of the flows and scaleswell with the number of nodes in the network. We show that,with our approach, the network operational cost defined in termsof both bandwidth and installed network function costs can bereduced and a higher acceptance rate can be achieved, while notinterrupting the flows

N'interrompez pas mes Chaines de Service Lorsque Je les Reconfigure

Network Functions Virtualization (NFV) enables the complete decoupling of network functions from proprietary appliances and runs them as software applications on general–purpose servers. NFV allows network operators to dynamically deploy Virtual Network Functions (VNFs).Software Defined Networking (SDN) introduces a logically centralized controller which maintains a global view of the network state. The centralized routing model of SDN jointly with the possibility of instantiating VNFs on–demand open the way for a more efficient operation and management of networks. In this paper, we consider the problem of reconfiguring network connections with the goal of bringing the network from a sub-optimal to an optimal operational state. We propose optimization models based on the make-before-break mechanism, in which a new path is set up before the old one is torn down. Our method takes into consideration the chaining requirements of the flows and scales well with the number of nodes in the network. We show that, with our approach, the network operational cost defined in terms of both bandwidth and installed network function costs can be reduced and a higher acceptance rate can be achievedLa virtualisation des fonctions réseau (NFV) permet le découplage complet des fonctions réseau des appareils propriétaires et leur exécution en tant qu’applications logicielles. NFV permet aux opérateurs de réseaux de déployer dynamiquement des fonctions de réseau virtuel (VNF). Le Software Defined Networking (SDN) introduit un contrôleur centralisé qui maintient une vue globale de l’état du réseau. Le modèle de routage centralisé du SDN et la possibilité d’instancier les VNF à la demande ouvrent la voie à une exploitation et une gestion plus efficaces des réseaux.Dans cet article, nous examinons le problème de la reconfiguration des connexions réseau dans le but de faire passer le réseau d’un état sous-optimal à un état opérationnel optimal. Nous proposons des modèles d’optimisation basés sur le mécanisme make-before-break, dans lequel un nouveau chemin est mis en place avant que l’ancien ne soit détruit. Ceci permet de ne pas avoir d’interruption du trafic. Notre méthode prend en compte les exigences de chaînage des flux et s’adapte bien au nombre de nœuds du réseau. Nous montrons qu’avec notre approche, le coût d’exploitation du réseau défini en termes de bande passante et de coûts des NFV installées peut être réduit tout en augmentant le taux d’acceptation des requêtes

Poster: Don't Interrupt Me When You Reconfigure my Service Function Chains

International audienceNetwork Functions Virtualization (NFV) enablesthe complete decoupling of network functions from proprietaryappliances and runs them as software applications on general–purpose servers. Service Function Chains (SFC) are paths with anordered sequence of network functions that have to be processed.In this paper, we consider the problem of reconfiguring SFCswith the goal of bringing the network from a sub-optimal toan optimal operational state. We propose optimization modelsbased on themake-before-breakmechanism, in which a new SFCis set up before the old one is torn down. Our method takes intoconsideration the chaining requirements of the flows and scaleswell with the number of nodes in the network. We show that,with our approach, the network operational cost defined in termsof both bandwidth and installed network function costs can bereduced and a higher acceptance rate can be achieved

Reconfiguration de chaînes de fonctions de services sans interruption

International audienceCe travail vise à montrer l’utilité de reconfigurer des chaînes de fonctions de services (SFC) dans le but d’améliorer le coût opérationnel du réseau. Nous proposons un modèle d’optimisation basé sur le mécanism emake-before-break, dans lequel l’ancien chemin n’est détruit que quand le nouveau chemin est complètement opérationnel, ceci afin de ne pas avoir d’interruption du trafic. Nous montrons qu’avec notre approche, nommée break-free, le coût opérationnel du réseau est réduit tout en augmentant le taux d’acceptation des SFC

Be Scalable and Rescue My Slices During Reconfiguration

International audienceModern 5G networks promise more bandwidth, less delay, and more flexibility for an ever increasing number of users and applications, with Software Defined Networking, Network Function Virtualization, and Network Slicing as key enablers. Within that context, efficiently provisioning network and cloud resources of a wide variety of applications with dynamic users' demands is a real challenge. In this work, we consider the problem of network slice reconfiguration. Reconfiguring from time to time network slices allows to reduce the network operational costs and to increase the number of slices that can be managed within the network. However, it impacts users' Quality of Service during the reconfiguration step. To solve this issue, we study solutions implementing a make-before-break scheme. We propose new models and scalable algorithms (relying on column generation techniques) that solve large data instances in few seconds

Reconfiguring Network Slices at the Best Time With Deep Reinforcement Learning

International audienceThe emerging 5G induces a great diversity of use cases, a multiplication of the number of connections, an increase in throughput as well as stronger constraints in terms of quality of service such as low latency and isolation of requests. To support these new constraints, Network Function Virtualization (NFV) and Software Defined Network (SDN) technologies have been coupled to introduce the network slicing paradigm. Due to the high dynamicity of the demands, it is crucial to regularly reconfigure the network slices in order to maintain an efficient provisioning of the network. A major concern is to find the best frequency to carry out these reconfigurations, as there is a tradeoff between a reduced network congestion and the additional costs induced by the reconfiguration. In this paper, we tackle the problem of deciding the best moment to reconfigure by taking into account this trade-off. By coupling Deep Reinforcement Learning for decision and a Column Generation algorithm to compute the reconfiguration, we propose Deep-REC and show that choosing the best time during the day to reconfigure allows to maximize the profit of the network operator while minimizing the use of network resources and the congestion of the network. Moreover, by selecting the best moment to reconfigure, our approach allows to decrease the number of needed reconfigurations compared to an algorithm doing periodic reconfigurations during the day

Contributions to topology discovery, self-healing and VNF placement in software-defined and virtualized networks

The evolution of information and communication technologies (e.g. cloud computing, the Internet of Things (IoT) and 5G, among others) has enabled a large market of applications and network services for a massive number of users connected to the Internet. Achieving high programmability while decreasing complexity and costs has become an essential aim of networking research due to the ever-increasing pressure generated by these applications and services. However, meeting these goals is an almost impossible task using traditional IP networks. Software-Defined Networking (SDN) is an emerging network architecture that could address the needs of service providers and network operators. This new technology consists in decoupling the control plane from the data plane, enabling the centralization of control functions on a concentrated or distributed platform. It also creates an abstraction between the network infrastructure and network applications, which allows for designing more flexible and programmable networks. Recent trends of increased user demands, the explosion of Internet traffic and diverse service requirements have further driven the interest in the potential capabilities of SDN to enable the introduction of new protocols and traffic management models. This doctoral research is focused on improving high-level policies and control strategies, which are becoming increasingly important given the limitations of current solutions for large-scale SDN environments. Specifically, the three largest challenges addressed in the development of this thesis are related to the processes of topology discovery, fault recovery and Virtual Network Function (VNF) placement in software-defined and virtualized networks. These challenges led to the design of a set of effective techniques, ranging from network protocols to optimal and heuristic algorithms, intended to solve existing problems and contribute to the deployment and adoption of such programmable networks.For the first challenge, this work presents a novel protocol that, unlike existing approaches, enables a distributed layer 2 discovery without the need for previous IP configurations or controller knowledge of the network. By using this mechanism, the SDN controller can discover the network view without incurring scalability issues, while taking advantage of the shortest control paths toward each switch. Moreover, this novel approach achieves noticeable improvement with respect to state-of-the-art techniques. To address the resilience concern of SDN, we propose a self-healing mechanism that recovers the control plane connectivity in SDN-managed environments without overburdening the controller performance. The main idea underlying this proposal is to enable real-time recovery of control paths in the face of failures without the intervention of a controller. Obtained results show that the proposed approach recovers the control topology efficiently in terms of time and message load over a wide range of generated networks. The third contribution made in this thesis combines topology knowledge with bin packing techniques in order to efficiently place the required VNF. An online heuristic algorithm with low-complexity was developed as a suitable solution for dynamic infrastructures. Extensive simulations, using network topologies representative of different scales, validate the good performance of the proposed approaches regarding the number of required instances and the delay among deployed functions. Additionally, the proposed heuristic algorithm improves the execution times by a fifth order of magnitude compared to the optimal formulation of this problem.Postprint (published version

A component-based virtual engineering approach to PLC code generation for automation systems

In recent years, the automotive industry has been significantly affected by a number of challenges driven by globalisation, economic fluctuations, environmental awareness and rapid technological developments. As a consequence, product lifecycles are shortening and customer demands are becoming more diverse. To survive in such a business environment, manufacturers are striving to find a costeffective solution for fast and efficient development and reconfiguration of manufacturing systems to satisfy the needs of changing markets without losses in production. Production systems within automotive industry are vastly automated and heavily rely on PLC-based control systems. It has been established that one of the major obstacles in realising reconfigurable manufacturing systems is the fragmented engineering approach to implement control systems. Control engineering starts at a very late stage in the overall system engineering process and remains highly isolated from the mechanical design and build of the system. During this stage, control code is typically written manually in vendor-specific tools in a combination of IEC 61131-3 languages. Writing control code is a complex, time consuming and error-prone process. [Continues.

Contributions to energy-aware demand-response systems using SDN and NFV for fog computing

Ever-increasing energy consumption, the depletion of non-renewable resources, the climate impact associated with energy generation, and finite energy-production capacity are important concerns worldwide that drive the urgent creation of new energy management and consumption schemes. In this regard, by leveraging the massive connectivity provided by emerging communications such as the 5G systems, this thesis proposes a long-term sustainable Demand-Response solution for the adaptive and efficient management of available energy consumption for Internet of Things (IoT) infrastructures, in which energy utilization is optimized based on the available supply. In the proposed approach, energy management focuses on consumer devices (e.g., appliances such as a light bulb or a screen). In this regard, by proposing that each consumer device be part of an IoT infrastructure, it is feasible to control its respective consumption. The proposal includes an architecture that uses Network Functions Virtualization (NFV) and Software Defined Networking technologies as enablers to promote the primary use of energy from renewable sources. Associated with architecture, this thesis presents a novel consumption model conditioned on availability in which consumers are part of the management process. To efficiently use the energy from renewable and non-renewable sources, several management strategies are herein proposed, such as the prioritization of the energy supply, workload scheduling using time-shifting capabilities, and quality degradation to decrease- the power demanded by consumers if needed. The adaptive energy management solution is modeled as an Integer Linear Programming, and its complexity has been identified to be NP-Hard. To verify the improvements in energy utilization, an optimal algorithmic solution based on a brute force search has been implemented and evaluated. Because the hardness of the adaptive energy management problem and the non-polynomial growth of its optimal solution, which is limited to energy management for a small number of energy demands (e.g., 10 energy demands) and small values of management mechanisms, several faster suboptimal algorithmic strategies have been proposed and implemented. In this context, at the first stage, we implemented three heuristic strategies: a greedy strategy (GreedyTs), a genetic-algorithm-based solution (GATs), and a dynamic programming approach (DPTs). Then, we incorporated into both the optimal and heuristic strategies a prepartitioning method in which the total set of analyzed services is divided into subsets of smaller size and complexity that are solved iteratively. As a result of the adaptive energy management in this thesis, we present eight strategies, one timal and seven heuristic, that when deployed in communications infrastructures such as the NFV domain, seek the best possible scheduling of demands, which lead to efficient energy utilization. The performance of the algorithmic strategies has been validated through extensive simulations in several scenarios, demonstrating improvements in energy consumption and the processing of energy demands. Additionally, the simulation results revealed that the heuristic approaches produce high-quality solutions close to the optimal while executing among two and seven orders of magnitude faster and with applicability to scenarios with thousands and hundreds of thousands of energy demands. This thesis also explores possible application scenarios of both the proposed architecture for adaptive energy management and algorithmic strategies. In this regard, we present some examples, including adaptive energy management in-home systems and 5G networks slicing, energy-aware management solutions for unmanned aerial vehicles, also known as drones, and applicability for the efficient allocation of spectrum in flex-grid optical networks. Finally, this thesis presents open research problems and discusses other application scenarios and future work.El constante aumento del consumo de energía, el agotamiento de los recursos no renovables, el impacto climático asociado con la generación de energía y la capacidad finita de producción de energía son preocupaciones importantes en todo el mundo que impulsan la creación urgente de nuevos esquemas de consumo y gestión de energía. Al aprovechar la conectividad masiva que brindan las comunicaciones emergentes como los sistemas 5G, esta tesis propone una solución de Respuesta a la Demanda sostenible a largo plazo para la gestión adaptativa y eficiente del consumo de energía disponible para las infraestructuras de Internet of Things (IoT), en el que se optimiza la utilización de la energía en función del suministro disponible. En el enfoque propuesto, la gestión de la energía se centra en los dispositivos de consumo (por ejemplo, electrodomésticos). En este sentido, al proponer que cada dispositivo de consumo sea parte de una infraestructura IoT, es factible controlar su respectivo consumo. La propuesta incluye una arquitectura que utiliza tecnologías de Network Functions Virtualization (NFV) y Software Defined Networking como habilitadores para promover el uso principal de energía de fuentes renovables. Asociada a la arquitectura, esta tesis presenta un modelo de consumo condicionado a la disponibilidad en el que los consumidores son parte del proceso de gestión. Para utilizar eficientemente la energía de fuentes renovables y no renovables, se proponen varias estrategias de gestión, como la priorización del suministro de energía, la programación de la carga de trabajo utilizando capacidades de cambio de tiempo y la degradación de la calidad para disminuir la potencia demandada. La solución de gestión de energía adaptativa se modela como un problema de programación lineal entera con complejidad NP-Hard. Para verificar las mejoras en la utilización de energía, se ha implementado y evaluado una solución algorítmica óptima basada en una búsqueda de fuerza bruta. Debido a la dureza del problema de gestión de energía adaptativa y el crecimiento no polinomial de su solución óptima, que se limita a la gestión de energía para un pequeño número de demandas de energía (por ejemplo, 10 demandas) y pequeños valores de los mecanismos de gestión, varias estrategias algorítmicas subóptimos más rápidos se han propuesto. En este contexto, en la primera etapa, implementamos tres estrategias heurísticas: una estrategia codiciosa (GreedyTs), una solución basada en algoritmos genéticos (GATs) y un enfoque de programación dinámica (DPTs). Luego, incorporamos tanto en la estrategia óptima como en la- heurística un método de prepartición en el que el conjunto total de servicios analizados se divide en subconjuntos de menor tamaño y complejidad que se resuelven iterativamente. Como resultado de la gestión adaptativa de la energía en esta tesis, presentamos ocho estrategias, una óptima y siete heurísticas, que cuando se despliegan en infraestructuras de comunicaciones como el dominio NFV, buscan la mejor programación posible de las demandas, que conduzcan a un uso eficiente de la energía. El desempeño de las estrategias algorítmicas ha sido validado a través de extensas simulaciones en varios escenarios, demostrando mejoras en el consumo de energía y el procesamiento de las demandas de energía. Los resultados de la simulación revelaron que los enfoques heurísticos producen soluciones de alta calidad cercanas a las óptimas mientras se ejecutan entre dos y siete órdenes de magnitud más rápido y con aplicabilidad a escenarios con miles y cientos de miles de demandas de energía. Esta tesis también explora posibles escenarios de aplicación tanto de la arquitectura propuesta para la gestión adaptativa de la energía como de las estrategias algorítmicas. En este sentido, presentamos algunos ejemplos, que incluyen sistemas de gestión de energía adaptativa en el hogar, en 5G networkPostprint (published version

Analytical approach to electrical distribution systems for aircraft

The More Electric Aircraft concept (MEA) is one of the most discussed topics of the recent decades inside the aircraft market. It aims to enable the migration towards more efficient aircraft while reducing the environmental impact by substituting the hydraulic, pneumatic and mechanical parts with their electrical counterparts. As the electrical systems became more complex, it is inevitable the need of a control unit that can manage the EPS under all the possible scenarios. For this reason, this thesis presents different study cases that a supervisor controller (SC) unit must address for guarantying the optimal EPS operations. In particular, the SC must be able to manage the network overloads, for preventing unwanted operations or oversized design of the on-board generators. Moreover, applying constant EPS monitoring, the SC must be able to solve critical scenarios by splitting the power flow on a different path. Apart from failure and critical tasks, the SC must be also employed in the EPS optimization using a mathematical algorithm that ensures the correct power spreading across each bus. After the introduction to the actual employed algorithms and used EPS, all the described cases are simulated inside Simulink® environment and a test bench is then configured to emulate a portion of a scaled EPS in the laboratory