This paper addresses the optimal placement of Multi-Access Edge Computing (MEC) nodes in 5G networks, aiming to meet stringent performance requirements while minimizing cost. It explores the impact of various Virtual Network Function (VNF) distribution strategies on overall network cost, specifically examining the 1:1, 1:N, and 1:N:K protection schemes. To tackle the MEC location problem, bi-objective nonlinear mathematical models are employed for exact solutions in small-scale networks, while the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied for larger networks. The results reveal that VNF distribution can significantly escalate network costs, with fully distributed VNFs incurring the highest expenses. However, the enhanced protection scheme demonstrates improved cost-efficiency. These findings highlight the critical role of strategic MEC placement and intelligent resource allocation in building scalable, resilient, cost-effective 5G infrastructures.

With the advent of 5G networks, telecommunications infrastructure providers (InP) have faced numerous challenges as they attempt to meet the stringent quality of service requirements. The placement of applications at the edge of the mobile network in Multi-access Edge Computing (MEC) and slicing techniques have provided powerful tools to enable networks to support these requirements. This paper studies the problem of locating MECs and slices in a 5G infrastructure protected by a 1:${N}$:${K}$protection scheme. The aim is to support high reliability and low latency requirements at a minimum cost. A bi-objective non-linear formulation is proposed, and a solution is derived by employing the non-dominated sorting genetic algorithm (NSGA)-II. Results show that the enhanced 1:${N}$:${K}$scheme is cost-effective. The proposal is evaluated on the basis of various levels of reliability, latency requirements, and probability of failure.

The support of stringent requirements such as ultra-low latency and ultra-reliability of the forthcoming 5G services poses several challenges to telecommunications infrastructure providers. Network Function Virtualization, multi-access edge computing (MEC), and network slicing capabilities can help the support of such requirements. However, a trade-off between the cost of resource deployment and the support of service requirements needs to be taken into account in the design of NFV-based 5G networks. In this paper, we investigate the MEC location problem, which aims at selecting locations to place MECs hosting protected slices. We propose a MEC location problem enhanced with 1: 1 and 1:Nprotection schemes for the provisioning of protected slices. In the 1: 1 scheme, protection is assured by reserving a backup slice for each tenant, whereas in the 1:Nscheme, a backup slice is shared amongNtenants. The problem is modeled as a multi-criteria optimization problem and solved by the employment of a multi-objective evolutionary non-dominated sorting genetic algorithm. A comparison between the 1: 1 and 1:Nprotection schemes is carried out in the context of 5G network slicing. Results show that the protection scheme 1: 1 can reduce the response time, at a higher deployment cost when compared to the 1:Nscheme.

This paper investigates the problem of locating edge devices in ultra-dense 5G network function virtualization-based small cell networks for the provisioning of reliable broadcasting services. The problem is formulated as a capacitated reliable facility location problem (CRFLP) with failure probability. The solution aims at placing the VNFs of broadcast transmissions optimally on selected edge devices in order to ensure high reliability and minimize the cost of providing broadcast services as well as the probability of loss of service requests. The CRFLP is a non-linear NP-hard problem and it is evaluated by using a multi-objective evolutionary algorithm. Two multi-objective metaheuristics are compared: the multi-objective particle swarm optimization and the nondominated sorting genetic algorithm. Results demonstrate that the proposed solution achieves high levels of reliability as well as low latency.

Mobile Operators have experienced a growth of demand of new services with strict requirements and features which imposes challenges to operate the mobile wireless network. To tackle these challenges, Telcos must provide flexible, dynamic network architecture, furnishing resiliency to ensure end-to-end (E2E) service continuity in case of resource failures. Virtualization of the core network elements of cellular Long-Term Evolution (LTE) and LTE evolved Multimedia Broadcast Multicast Service (eMBMS) technology has been proposed as a key solution to cope with these new demands. By employing point-to-multipoint services to any wireless device, the LTE eMBMS, allows Mobile Operators (MOs) to send a single stream of data to all mobile users in a specific area. The LTE eMBMS is a potential E2E service technology to leverage costumer experience as well as to optimize use of network resources. We propose to improve the reliability of LTE eMBMS E2E services via redundancy, virtualized network functions (VNF), and autonomic service placement. In this paper, we introduce a redundancy seriesparallel model to improve reliability of LTE eMBMS services. The model is formulated as a VNF redundancy allocation problem (RAP). We employed the particle swarm optimization (PSO) technique to solve the RAP problem to evaluate the performance of the proposed model.