Over the last thirty years, it has been demonstrated that the chaotic behaviour of Internet traffic was irregular and exhibited a contagion that radiated across the TCP/IP stack, referred to as self-similarity (SS). However, most studies examined this behaviour within a single computer network. With the rise of virtual networks, it was observed that SS could be tamed by employing fractal decision variables per alternative. Consequently, a fractal evaluation framework emerged as a specialist system employing multiplicative Data Envelopment Analysis (DEA) models to compare the virtual networks, which served as the Decision Making Units (DMUs). This led to the building of a unique global tool named FRANCISCO: FRactal Network Cloud Infrastructure Service Comparison and Optimization, which conducted comparisons using classical super-efficiency models of DEA and performed exploratory data analysis for each one of the DMUs. Nevertheless, the initial development of FRANCISCO left some gaps; therefore, the contributions of this work are: a) to enhance the usability of FRANCISCO by creating help options; b) to select public datasets to facilitate the use and understanding in the operationalisation of the tool, and; c) to implement static and dynamic multiplicative models of DEA.