Cybersecurity in telecommunication networks often leads to hard combinatorial optimization problems that are challenging to solve with classical methods. This work investigates the practical feasibility of using quantum annealing to address the Restricted Vertex Minimum Multicut Problem. The problem is formulated as a Quadratic Unconstrained Binary Optimization model and implemented on D-Wave’s quantum annealer. Rather than focusing on solution quality alone, we analyze key aspects of the quantum workflow including minor embedding techniques, chain length, topology constraints, chain strength selection, unembedding procedures, and postprocessing. Our results show that quantum annealing faces substantial hardware-level constraints limitations in embedding and scalability, especially for large in-stances, while hybrid quantum-classical solvers provide improved feasibility. This study offers a realistic assessment of the D-Wave system’s current capabilities and identifies crucial parameters that govern the success of quantum optimization in cybersecurity-related network problems.

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.

The paper focuses on the international wholesale roaming market and the strategic problem, for a multinational group of mobile network operators, of making steering decisions based on uncertain traffic forecasts. The subsidiaries of the multinational group aim at optimizing their costs by distributing their subscribers travelling abroad between the partner operators of the visited countries, and subsequently distributing the roaming traffic. However there is uncertainty in the expected wholesale roaming cost at the end of the year due to inaccurate roaming traffic forecasts. We propose an approach for i) measuring the impact of the worst-case traffic forecasts scenario on the nominal steering decisions in terms of forecasted cost, ii) computing the steering decisions minimizing the worst-case roaming cost and thus assessing the robustness of the nominal steering decisions by comparison. Experiments show the impacts of the uncertainty modeling on the nominal steering decisions.

In many fields in computational sustainability, applications of POMDPs are inhibited by the complexity of the optimal solution. One way of delivering simple solutions is to represent the policy with a small number of alpha-vectors. We would like to find the best possible policy that can be expressed using a fixed number N of alpha-vectors. We call this the N-POMDP problem. The existing solver alpha-min approximately solves finite-horizon POMDPs with a controllable number of alpha-vectors. However alpha-min is a greedy algorithm without performance guarantees, and it is rather slow. This paper proposes three new algorithms, based on a general approach that we call alpha-min-2. These three algorithms are able to approximately solve N-POMDPs. Alpha-min-2-fast (heuristic) and alpha-min-2-p (with performance guarantees) are designed to complement an existing POMDP solver, while alpha-min-2-solve (heuristic) is a solver itself. Complexity results are provided for each of the algorithms, and they are tested on well-known benchmarks. These new algorithms will help users to interpret solutions to POMDP problems in computational sustainability.