FRANCISCO 2.0: Increasing the Usability and Appending Multiplicative DEA Models for Internet Services Forecasting Over Time

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.