Optimal Network Service Chain Provisioning

International audienceService chains consist of a set of network services, such as firewalls or application delivery controllers, which are interconnected through a network to support various applications. While it is not a new concept, there has been an extremely important new trend with the rise of Software-Defined Network (SDN) and Network Function Virtualization (NFV). The combination of SDN and NFV can make the service chain and application provisioning process much shorter and simpler. In this paper, we study the provisioning of service chains jointly with the number/location of Virtual Network Functions (VNFs). While chains are often built to support multiple applications, the question arises as how to plan the provisioning of service chains in order to avoid data passing through unnecessary network devices or servers and consuming extra bandwidth and CPU cycles. It requires choosing carefully the number and the location of the VNFs. We propose an exact mathematical model using decomposition methods whose solution is scalable in order to conduct such an investigation. We conduct extensive numerical experiments, and show we can solve exactly the routing of service chain requests in a few minutes for networks with up to 50 nodes, and traffic requests between all pairs of nodes. Detailed analysis is then made on the best compromise between minimizing the bandwidth requirement and minimizing the number of VNFs and optimizing their locations using different data sets

Service placement and request routing in MEC networks with storage, computation, and communication constraints

The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network edge, in proximity to the end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be pre-stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in dense MEC networks with multidimensional constraints. We show that this problem generalizes several well-known placement and routing problems and propose an algorithm that achieves close-to-optimal performance using a randomized rounding technique. Evaluation results demonstrate that our approach can effectively utilize available storage, computation, and communication resources to maximize the number of requests served by low-latency edge cloud servers

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

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

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

Be Scalable and Rescue My Slices During Reconfiguration

Modern 5G networks promise more bandwidth ,less delay, and more exibility 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, effciently provisioning network and cloud resources of a wide variety of applications with dynamic users' demands is a rea lchallenge. In this work, we consider the problem of network slice reconfiguration. Reconfiguring regularly network slices allows to reduce the network operational cost. However, it impacts users' Quality of Service by changing the routing. To solve this issue, we study solution simplementing 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.Les réseaux 5G modernes promettent plus de bande passante, moins de délai et plus de flexibilité pour un nombre toujours croissant d'utilisateurs et d'applications, avec la programmation logicielle des réseaux (SDN), la virtualisation des fonctions réseau, le découpage du réseau en slices comme facteurs clés de réussite. Dans ce contexte, le provisionnemente efficace des ressources réseau d'une grande variété d'applications avec les demandes des utilisateurs dynamiques est un véritable défi. Dans cet ravail, nous considérons le problème de la reconfiguration des slices réseau. La reconfiguration régulière des slices permet de réduire le coût opérationnel du réseau. Cependant, il a un impact sur le Qualité de service des utilisateurs en modifiant le routage. Pour résoudre ce problème, nous étudions des solutions mettant en oeuvre un schéma de reconfiguration des slices avec une technique make-before-break. Nous proposons de nouveaux modèles et algorithmes s'appuyant sur des techniques de génération de colonnes qui permettent de résoudre de grandes instances du problème en quelques secondes

A Study on Efficient Service Function Chain Placement in Network Function Virtualization Environment

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