derations and focusing on the deployment of MEC in a fully virtualized 5G system involving the operation of multiple slices, one possible method of slice-centric system operation would be to optimize the allocation of MEC applications (modeled as VNFs) across the edge cloud. Allocation of MEC applications should be performed according to a slice-aware strategy, in order to meet the E2E performance requirements of a given slice, which are assumed to be part of a Service Level Agreement (SLA) between the network operator and a customer. A possible solution may involve an iterative procedure, involving both 3GPP (e.g. a mobile operator's OSS) and MEC system functional entities, as specified in MEC003 (i.e. the MEC system's MEAO, and the NFVO). The goal of such a solution is a slice-efficient allocation of virtualized resources, especially in the existence of user mobility. According to this procedure, measurements of delay components relevant to different system domains (3GPP, NFV, MEC) may be collected and then processed with the aim of identifying the performance "bottleneck" and allocating more resources to the entity/entities responsible for E2E performance degradation. 6.4.5 Inter-MEC System Communication In this section, we focus on a deployment scenario involving multiple MEC systems, as specified in MEC003. An illustrative example (related to the automotive domain) considers a MEC-enabled V2X communication system, where a road operator (equivalently, an ITS operator) aims to offer V2X services in a cross-country, cross-operator and cross-vendor environment. Figure 6.4.8 illustrates an example scenario, in which there are two mobile operators deploying an ITS network, with MEC hosts co-located to the corresponding radio access nodes. One of the most challenging (but also most frequent) situations is when the ITS operator has to provide the same V2X service to all vehicles connected to different mobile opera- tors, even in the temporary absence of radio coverage. This case is often accompanied by the presence 