IP video traffic is forecast to be 82% of all IP traffic by 2022. Traditionally, Content Distribution Networks (CDN) were used extensively to meet the quality of service levels for IP video services. To handle the dramatic growth in video traffic, CDN operators are migrating their infrastructure to the cloud and fog in order to leverage its greater availability and flexibility. For hyper-scale deployments, energy consumption, cache placement, and resource availability can be analyzed using simulation in order to improve resource utilization and performance. Recently, a discrete-time simulator for modelling hierarchical virtual CDNs (vCDNs) was proposed with reduced memory requirements and increased performance using multi-core systems to cater to the scale and complexity of these networks. The first iteration of this discrete-time simulator featured a number of limitations impacting accuracy and applicability: it supports only tree-based topology structures, the results are computed per level, and requests of the same content differ only in time duration. In this paper, we present an improved simulation framework that (a) supports graph-based network topologies, (b) requests have been reconstituted for differentiation of requirements, and (c) statistics are now computed per site and network metrics per link, improving the granularity and parallel performance. Moreover, we also propose a two-phase optimization scheme that makes use of simulation outputs to guide the search for optimal cache placements. In order to evaluate our proposal, we simulate a vCDN network based on real traces obtained from the BT vCDN infrastructure and analyze performance and scalability aspects.