Prediction of the best alternative for cloud network services delivering using fractals variables and super-efficiency DEA models for time-series data comparison

Published: 25 March 2024| Version 1 | DOI: 10.17632/29txvz235y.1
Francisco Daladier Marques Júnior


This dataset is based on active measurements obeying RFC 2544 and RFC 6815, forming 150 tunneled virtual networks to be compared. The virtual networks were assembled using the VXLAN protocol in an IaaS within DevStack (ALL-IN-ONE) running on a VM in VirtualBox on an Ubuntu 20.4 operating system of a computer with an Intel i7 7500U processor, 2.9GHz clock, and 16GB of RAM. OpenStack-Neutron with virtual switch SDN (OpenvSwitch) was used to interconnect the VMs under analysis. The benchmarking tools iperf, VBoxManage, and lscpu (to capture the temperature of each core) were used for active measurements. In the experiments, two Guest VMs were utilized, with one serving as a traffic generator (TG) and the other as a device under test (DUT), but the DUT runs within a Docker container acting as a server. In the DUT, we varied the number of vCPUs (1,2,4), the amount of vRAM (512MB, 1GB, 2GB, 4GB, and 8GB), and the fifteen Linux TCP congestion control algorithms (BIC, BBR, CDG, CUBIC, DCTCP, ILLINOIS, HYBLA, HTCP, LP, NV, VEGAS, VENO, SCALABLE, WESTWOOD, and YEAH) making up the 150 decision-making units (DMU). The virtual networks (DMU) were ranked using the super-efficiency model of data envelopment analysis (DEA) with variable return to scale and input-oriented. The decision variables listed for ranking were: a) inputs – X1) TCP bandwidth fractal dimension; X2) core0 fractal dimension, X3) core1 fractal dimension, X4) core0 temperature average, and X5) core1 temperature average, and b) outputs– Y1) core 0 hurst, Y2) core 1 Hurst, Y3) TCP bandwidth average, and Y4) TCP bandwidth Hurst. All data per DMU were separated by a folder containing their respective time series. Thus, we conclude that the correct DMU was chosen to provide optimal virtual network services over time. All scripts used to extract decision variables are available in this dataset. Calculating fractal dimensions was madogram and the Hurst parameter was calculated using R/S.


Steps to reproduce

1) Use the FRANCISCO (FRActal Network Cloud Infrastructure Service Comparison and Optimisation) web tool available at URL: and read its paper for clarification available at URL: or 2) Run the R scripts to obtain the fractal decision variables available on each folder inside the docker main folder; 3) All time-series data of each one of the 150 virtual networks are available in its respective folders.


Instituto Federal de Educacao Ciencia e Tecnologia da Paraiba


Cloud Computing, Data Envelopment Analysis, Big Data, Fractal, Decision Making, Software-Defined Networking