In 5G networks, physical resources can be virtualized and allocated to separate virtual networks (or network slices), with distinct requirements. The Virtual Network Embedding (VNE) problem consists in finding the optimal mapping of virtual resources (virtual links and nodes) onto a physical infrastructure. A recent trend consists in virtualizing 5G networks using Kubernates (K8s), a popular virtualization technology. In this paper we perform an experimental study to show the limit of using the standard K8s deployment strategy when dealing with dynamically arriving slices in a heavy loaded setting. By deploying the virtual components of a slice one by one, standard K8s is prone to wasting resources and energy due to partially deploying slices that, at the end, are found to be infeasible, due to lack of available resources. We propose an alternative K8s deployment strategy that first solves VNE via a Genetic Algorithm and then, for each slice, deploys either all its components or none. Our experimental results show a notable improvement in slice acceptance, energy efficiency and deployment time. Our work shows that it is necessary to adapt cloud native technologies to the specific requirements of telecommunication scenarios, as they are different from the cloud ones for which such technologies were originally developed.