Super Multiplicative VRS DEA (SMVDEA) input-oriented model with raw measurements results
Super Multiplicative VRS DEA input-oriented model results obtained with 660 DMUs using apache bench tool for evaluating video stream files on two main webservers from the market, that are Apache2 vs Nginx, varying 15 TCP congestion control algorithms on Xen hypervisor. Abstract: The prediction of the best set of tools for delivering network services is always a hot topic of research, mainly on the Internet where the population changes its interest in the type of services to be consumed rapidly. Moreover, randomness is a challenge for all kinds of prediction, even though specifically on computer networks in the 90´s decade the self-similarity concept on computer networks brought the memory over time on spatiotemporal data. The self-similarity's memory and fractal dimension are important indexes to see the difference in performance and stability among a set of time series data over time. Recently, it was discovered on virtual networks that change the set of tools or virtual network hypervisors for delivering services there is a distinct fractal behaviour on network services providers. Then, the choice of the best virtual network hypervisor is mandatory to deliver the best way to provision virtual network services along the time. By the way, the researchers discovered along with the history a series of analytic ways of forecasting services based on time series, one of them is data envelopment analysis (DEA). DEA are nonparametric multicriteria decision-making models that use decision variables to make an accurate judgment of decision-making units (DMU). The main contribution of the paper is to devise a new super-efficiency multiplicative DEA model for the prediction of the best DMU varying the TCP congestion control algorithms and virtual network settings as vCPU (1, 2, 3, and 4) and vRAM (1,2, and 4) with network and fractal indexes as decision variables. The experiments were conducted on Xen type-I hypervisor to acquire the web traffic by using the apache-bench benchmarking tool for obtaining each decision variable from DMUs for DEA ranking.
Steps to reproduce
Measurements were made using two guest virtual machines (VM) on Xen hypervisor, in an isolated test environment, where one of them had a Docker container running Apache2 or Nginx for comparison. The main guest VM from the Docker container varied 15 TCP congestion control algorithms for evaluating video stream files for service delivery. In the Docker VM, one varied the amount of vCPU (1,2,3, and 4) and vRAM (1 and 4 GB). Another VM ran the apache bench tool to capture the decision variables from all decision-making units. Finally, the most efficient of the alternatives/DMUs was accurately selected by using the SMVDEA ranking model herein devised. All results obtained are available inside the "raw virtual network measurements" folder.