Innovative services with strict requirements are expected in the fifth generation (5G) of mobile networks and beyond. For example, the Ultra-Reliable Low-Latency Communication (URLLC) requires up to 1 ms latency, end-to-end security, and reliability of up to 99.999%. The Multi-access Edge Computing (MEC) promises to support the delivery of URLLC services by providing computing and storage resources in the proximity of user equipment. The data which previously needed to be processed and stored in the cloud systems can be kept at the edge network, decreasing the total latency and increasing the context-awareness, security, and dependability. Vastly available resources, which are available from cloud to edge, must be appropriately allocated to deliver a service efficiently. The resource allocation problem in MEC for 5G-and-beyond networks can be formulated differently, depending on the nature of the problem. This survey outlines the resource allocation problem as a proper problem formulation, which can be addressed by target, resource type, resource issue, and the considered assumptions. Moreover, this paper also describes the open issues and future directions for MEC resource allocation based on the state of the art on this research topic.

Multi-Access Edge Computing (MEC) and network slicing two of the key enabling technologies of the Fifth Generation (5G) of cellular network. MEC helps to reduce latency, offload the cloud, and allow context-awareness. Network slicing allows to create heterogeneous services on top of shared infrastructures. Slice brokers are emerging intermediate entities that take the resources from the infrastructure providers and make slices for the tenants. In this scenario, a slice broker needs to manage the resource and create the slices in order to maximize its revenue to cover the cost and increase the profit. In this work, we consider that the demand of the slice tenant is depending on the price of the slices. Therefore, we formulate a slice allocation problem that consider this demand-price dynamic. Moreover, we consider the presence of adversary that want to compromise the decision process. In order to solve the problem, we propose a multi-agent environment, where some agents cooperate to learn the revenue model and maximize the revenue. Finally, we evaluate the effectiveness of the proposed solution by comparing it with reference solutions. The results highlight that a notable increment of the revenue can be obtained by using our solution.

The introduction of 5G technology enables new V2X services requiring reliable and extremely low latency communications. To satisfy these requirements computing elements need to be located at the edge of the network, according to the Multi-access Edge Computing (MEC) paradigm. The user mobility and the MEC approach lead to the need to carefully analysing the procedures for the migration of applications necessary to maintain the service proximity, fundamental to guarantee low latency. The paper provides an experimental comparison of three different migration strategies. The comparison is performed considering three different containerized MEC applications that can be used for developing V2X services. The experimental study is carried out by means of a testbed where the user mobility is emulated by the ETSI MEC Sandbox. The three strategies are compared considering the viability, the observed service downtime, and the amount of state preserved after the migration. The obtained results point out some trade-offs to consider in any migration scenario.

The 5G-MEC architecture increases the heterogeneity and dynamicity of the available resources, presenting unique and competing challenges to researchers, network designers, and application developers. Recent studies indicate AdvantEDGE as an interesting emulation platform to investigate these challenges. The paper presents a particular example of AdvantEDGE usage. A testbed composed of the emulated 5G-MEC architecture and the VideoLAN application allows to analyse the performance of alternative handover strategies, developed by using a multi-objective approach. The study shows how AdvantEDGE allows a deep analysis of the behaviour of the different strategies during the emulated user mobility, giving the possibility of measuring performance parameters at different layers, i.e. IP, application, and end-user.