# End-to-End Performance Evaluation of MEC Deployments in 5G Scenarios
For all these reasons, the focus of this paper is on MEC deployment in mobile networks, also due to the huge business and technology impact of 5G systems on future communications. The deployment of the MEC infrastructure should meet different, contrasting needs. On one hand, the infrastructure owner should ensure that its hosts are used efficiently: to this aim, it would be preferable to pool resources relatively far from the users, so that temporal and spatial service usage variability can be met by leveraging statistical multiplexing, aggregating fewer computing resources. On the other hand, low-latency services should, by definition, be hosted as close as possible to the user clients, which pulls towards a capillary distribution of computing resources. Other factors, such as technology constraints (e.g., maximum size of MEC hosts), logistic constraints (e.g., accessibility of sites), or cost aspects (i.e., CAPEX and OPEX), will also play a role. The optimal trade-off point will depend on all these tradeoffs and also on the type of services considered. Finally, the actual MEC penetration will also be influenced by the dominant type of user access technology in a specific area. Accordingly, since 5G access is expected to dominate in the near future, there is a pressing need for a sound evaluation of MEC services and deployments in 5G networks. Such an evaluation should factor in user-related performance metrics, related to both the computation and communication segments of a MEC service. For instance, the round-trip time experienced by a user for a request-response service will include both the communication latency, i.e., both access, core, and backhaul traversing up to a (possibly far away) MEC host, and to the computation time spent by the MEC app on the host itself, which in turn will depend on contention of shared computing resources (e.g., CPU, memory, storage). From this perspective, it is quite clear that deploying MEC hosts at different locations will affect both the communication latency and the contention on resources (since it will alter the number of users sharing those resources). For all these reasons, performance assessment is key for the evaluation of MEC deployment options. In particular, a proper end-to-end (E2E) evaluation should include reliable models of both the network and the computing environment. This is not easy to achieve in practice, due to the presence of many components, from access to edge and core network, and the need to cover not only PHY and MAC layers, but also traffic models in system-level simulations, and a proper workload modeling of the specific MEC app, related to the use-case under evaluation. Some existing works (e.g., refs. [8; 9]) rely on numerical evaluations of the system performance, using analytical models. Few simulation tools allow one to simulate applications and services communicating through a 5G network, notably ns3-based 5G Lena [10] and OMNeT++-based Simu5G, both of which quite recent. To the best of our knowledge, ns3 does not include models of MEC hosts. Simu5G, on the other hand, does include a MEC host model [11], but the resource contention at the latter is not modeled satisfactorily. On the other hand, there are cloud/edge computing simulators, e.g., refs. [12; 13], which model the computation part but not the communication one. Intel CoFluent [14] is one such tool. However, a unified framework, including both communication and computation, is missing. In this paper, we evaluate the performance of MEC services, specifically in-vehicle infotainment, used by users accessing them through a 5G network in mobility. We focus on the impact of the various 5G deployment alternatives on the performance of the considered MEC service and their interplay with the capacity provided within the MEC infrastructure. We claim that an effective performance evaluation of MEC services cannot be achieved by analyzing the RAN part through network-only simulation alone.