Provably Efficient Algorithms for Placement of Service Function Chains with Ordering Constraints

International audienceA Service Function Chain (SFC) is an ordered sequence of network functions, such as load balancing, content filtering, and firewall. With the Network Function Virtualization (NFV) paradigm, network functions can be deployed as pieces of software on generic hardware, leading to a flexibility of network service composition. Along with its benefits, NFV brings several challenges to network operators, such as the placement of virtual network functions. In this paper, we study the problem of how to optimally place the network functions within the network in order to satisfy all the SFC requirements of the flows. Our optimization task is to minimize the total deployment cost. We show that the problem can be seen as an instance of the Set Cover Problem, even in the case of ordered sequences of network functions. It allows us to propose two logarithmic factor approximation algorithms which have the best possible asymp-totic factor. Further, we devise an optimal algorithm for tree topologies. Finally, we evaluate the performances of our proposed algorithms through extensive simulations. We demonstrate that near-optimal solutions can be found with our approach

Algorithmes d'approximation pour le placement de chaînes de fonctions de services avec des contraintes d'ordre

A Service Function Chain (SFC) is an ordered sequence of network functions, such as load balancing, content filtering, and firewall. With the Network Function Virtualization (NFV) paradigm, network functions can be deployed as pieces of software on generic hardware, leading to a flexibility of network service composition. Along with its benefits, NFV brings several challenges to network operators, such as the placement of virtual network functions. In this paper, we study the problem of how to optimally place the network functions within the network in order to satisfy all the SFC requirements of the flows. Our optimization task is to minimize the total deployment cost.We show that the problem can be seen as an instance of the Set Cover Problem, even in the case of ordered sequences of network functions. It allows us to propose two logarithmic factor approximation algorithms which have the best possible asymptotic factor. Further, we devise an optimal algorithm for tree topologies. Finally, we evaluate the performances of our proposed algorithms through extensive simulations. We demonstrate that near-optimal solutions can be found with our approach.Le modèle des réseaux programmables (Software Defined Networks), permet de centraliser la gestion du réseau sur un ou plusieurs contrôleurs et par conséquent de découpler la fonction de contrôle des flux de données. Ce paradigme permet aux opérateurs de réseaux de télécommunications d'offrir des services réseaux complexes et flexibles. Un service se modélise alors comme une chaîne de fonctions réseaux (firewall, compression, contrôle parental ...) qui doivent être appliquées séquentiellement à un flot de données. Dans cet article, nous étudionsle problème du placement de fonctions de services qui consiste à determiner sur quels noeuds localiser les fonctions afin de satisfaire toutes les demandes de service, de façon à minimiser le coût de déploiement.Nous montrons que le problème peut être ramené à un problème de Set Cover, même dans le cas de séquences ordonnées de fonctions réseau. Cela nous permet de proposer deux algorithmes d'approximation à facteur logarithmique, ce qui est le meilleur facteur possible. De plus, nous proposons un algorithme optimal dans le cas particulier ou la topologie des demandes est un arbre. Finalement, nous évaluons les performances de nos algorithmes par simulations. Nous montrons ainsi qu'en pratique, des solutions presque optimales peuvent être trouvées avec notre approche

Getting the Most Out of Your VNFs: Flexible Assignment of Service Priorities in 5G

Through their computational and forwarding capabilities, 5G networks can support multiple vertical services. Such services may include several common virtual (network) functions (VNFs), which could be shared to increase resource efficiency. In this paper, we focus on the seldom studied VNF-sharing problem, and decide (i) whether sharing a VNF instance is possible/beneficial or not, (ii) how to scale virtual machines hosting the VNFs to share, and (iii) the priorities of the different services sharing the same VNF. These decisions are made with the aim to minimize the mobile operator's costs while meeting the verticals' performance requirements. Importantly, we show that the aforementioned priorities should not be determined a priori on a per-service basis, rather they should change across VNFs since such additional flexibility allows for more efficient solutions. We then present an effective methodology called FlexShare, enabling near-optimal VNF-sharing decisions in polynomial time. Our performance evaluation, using real-world VNF graphs, confirms the effectiveness of our approach, which consistently outperforms baseline solutions using per-service priorities

Low-latency and Resource-efficient Service Function Chaining Orchestration in Network Function Virtualization

© 2014 IEEE. Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this article, we propose an SFC deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS-based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing