Virtual Network Embedding (VNE) is an optimization problem at the core of many modern network telecommunication technologies related to the implementation of virtual networks, such as Network Slicing. The VNE problem consists in finding an optimal assignment of virtual demands to physical resources, encompassing simultaneous placement and routing decisions.We study the offline version of the VNE, which arises in the context of decision-making for resource allocation and network slice planning. For large networks, the heuristics of the literature often struggle to find solutions, especially when available resources (on nodes and edges) are sparse.To address these challenges, we explore mathematical programming approaches. Since the classical Flow Formulation provides a weak linear relaxation, we consider a novel formulation, based on a partition of the virtual graph into smaller virtual subgraphs. Since this formulation has an exponential number of variable, its linear relaxation can be solved with Column Generation. We devise a Price-Branch heuristic able to solve large instances, while providing optimality gap. The resulting computational experiments indicate our Price-Branch heuristic is often the only algorithm able to find a solution from a certain instance size, largely outperforming the Flow Formulation or literature heuristics.

The advent of Network Function Virtualization (NFV) and virtualized Content Delivery Network (vCDN) has revolutionized the deployment of resources at the edge of the network, offering a more efficient alternative to traditional CDN architectures. However, this approach introduces the challenge of resource limitations at the edge, making effective resource allocation a critical issue. This paper tackles the problem of placement of virtual network functions (VNF) by proposing a planning strategy to assign end-users access points to edge servers where vCDN functions are deployed, ensuring compliance with Service Level Agreement (SLA) while minimizing the energy consumption. We show that the problem is NP-hard and then propose a Mixed Integer Linear Program (MILP) to formulate our problem, making use of a non-linear energy model from the literature to estimate the energy footprint. We evaluate the proposal leveraging real traffic demand data from a nationwide mobile operator to model realistic network conditions. Additionally, we investigate the impact of varying the number of edge servers on the overall energy footprint. Our results demonstrate the effectiveness of the proposed optimization strategy in reducing energy consumption while maintaining the required quality of service compared to a baseline approach.

Abstract Given a bi‐directed graph modeling a telecommunication network, and a set of origin‐destination pairs representing traffic requests (commodities) along with their associated Service Function Chains (SFCs), the Virtual Network Function Placement and Routing Problem (VNFPRP) aims to find, for each commodity, one latency‐constrained routing path that visits the required Virtual Network Functions in a specific order. The function installation costs together with the node activation costs have to be minimized. In this paper, we present two extended Mixed Integer Programming (MIP) formulations to model the VNFPRP. For each formulation we define the master problem, the pricing problem, the associated Lagrangian bound and a specific branching scheme, in order to derive an efficient Branch‐and‐Price algorithm. We also provide several families of valid inequalities to strengthen the LP‐relaxation bounds. Computational results are reported comparing the performance of the two Branch‐and‐Price algorithms with a compact MIP formulation and its Branch‐and‐Benders‐cut implementation on a set of SNDlib instances representing telecommunication networks.

The adoption of 5G services depends on the capacity to provide high-value services. In addition to enhanced performance, the capacity to deliver Security Service Level Agreements (SSLAs) and demonstrate their fulfillment would be a great incentive for the adoption of 5G services for critical 5G Verticals (e.g., service suppliers like Energy or Intelligent Transportation Systems) subject to specific industrial safety, security or service level rules and regulations (e.g., NIS or SEVESO Directives). Yet, responsibilities may be difficult to track and demonstrate because 5G infrastructures are interconnected and complex, which is a challenge anticipated to be exacerbated in future 6G networks. This paper describes a demonstrator and a use case that shows how 5G Service Providers can deliver SSLAs to their customers (Service Owners) by leveraging a set of network enablers developed in the INSPIRE-5Gplus project to manage their accountability, liability and trust placed in subcomponents of a service (subcontractors). The elaborated enablers are in particular a novel sTakeholder Responsibility, AccountabIity and Liability deScriptor (TRAILS), a Liability-Aware Service Management Referencing Service (LASM-RS), an anomaly detection tool (IoT-MMT), a Root Cause Analysis tool (IoT-RCA), two Remote Attestation mechanisms (Systemic and Deep Attestation), and two Security-by-Orchestration enablers (one for the 5G Core and one for the MEC).

We model the network slice provisioning as an optimization problem including novel mapping and provisioning requirements rising with new radio and core function placement policies. We propose an open-access framework based on an MILP formulation that encompasses flexible functional splitting, with possibly different splitting for different slices and slice subnets, while taking into account different network sharing policies from 5G specifications. We also consider novel mapping and continuity constraints specific to the 5G architectures and beyond. We show by numerical simulations the impact of taking into full and partial consideration these peculiar novel technical constraints.

In this work, we model the network slice provisioning as an optimization problem including novel mapping and provisioning requirements rising with new 5G radio and core function placement policies. We propose an MILP-based formulation that joins different functional splitting strategies with different network function sharing policies and novel mapping continuity constraints from 5G specifications. We show by numerical simulations the impact of taking into full and partial consideration these peculiar sets of novel technical constraints.

International audience

International audience

Software‐Defined Networking and Network Function Virtualization are two paradigms that offer flexible software‐based network management. Service providers are instantiating Virtualized Network Functions, for example, firewalls, DPIs, gateways—to highly facilitate the deployment and reconfiguration of network services with reduced time‐to‐value. They use Service Function Chaining technologies to dynamically reconfigure network paths traversing physical and virtual network functions. Providing a cost‐efficient virtual function deployment over the network for a set of service chains is a key technical challenge for service providers, and this problem has recently caught much attention from both Industry and Academia. In this article, we propose a formulation of this problem as an Integer Linear Program that allows one to find the best feasible paths and virtual function placement for a set of services with respect to a total financial cost, while taking into account the (total or partial) order constraints for Service Function Chains of each service and other constraints such as end‐to‐end latency, anti‐affinity rules between network functions on the same physical node and resource limitations in terms of network and processing capacities. Furthermore, we propose a heuristic algorithm based on a linear relaxation of the problem that performs close to optimum for large scale instances. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 71(2), 97–106 2018