Social-like analysis on Virtual Machine communication traces

We apply social network analysis methods on communication traces, collected from Virtual Machines (VMs) located in computing infrastructures, like a data center. Our aim is to identify important VMs, for example VMs that consume more energy or require more computational capacity, bandwidth, etc, than other VMs. We believe that this approach can handle the large number of VMs present in computing infrastructures and their interactions in the same way social interactions of millions of people are analyzed in today's social networks. We are interested in identifying measures that can locate important VMs or groups of interacting VMs, missed through other usual metrics and also capture the time-dynamicity of their interactions. In our work we use real traces and evaluate the applicability of the considered methods and measures.