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.

通过使用 Apache Bench 工具对市场上两个主要网络服务器（Apache2 与 Nginx）的视频流文件进行评估，获得基于 Super Multiplicative VRS DEA 输入导向模型的 660 个决策单元（DMU）的超效率乘法 DEA 模型结果。摘要：对于预测提供网络服务的最佳工具集，这始终是研究的热点话题，尤其是在互联网领域，用户对服务的兴趣类型快速变化。此外，随机性对所有类型的预测都是一个挑战，尽管在 90 年代，计算机网络中的自相似性概念为时空数据带来了时间上的记忆。自相似性的记忆和分形维度是观察一组时间序列数据随时间性能和稳定性的重要指标。最近，在虚拟网络中发现，改变提供服务的工具集或虚拟网络管理程序（如 vCPU（1、2、3 和 4）和 vRAM（1、2 和 4））将导致网络服务提供商表现出独特的分形行为。因此，选择最佳的虚拟网络管理程序对于在时间维度上提供最佳的虚拟网络服务至关重要。顺便提一下，研究人员在历史数据的基础上发现了一系列基于时间序列的服务预测分析方法，其中之一是数据包络分析（DEA）。DEA 是一种非参数多标准决策模型，使用决策变量对决策单元（DMU）进行精确的判断。本文的主要贡献是设计了一种新的超效率乘法 DEA 模型，用于预测在 TCP 拥塞控制算法和虚拟网络设置（如 vCPU 和 vRAM）变化下的最佳 DMU，以网络和分形指数作为决策变量。实验是在 Xen 类型 I 虚拟管理程序上进行的，通过使用 Apache-bench 基准测试工具获取网络流量，并为 DEA 排名从 DMU 获取每个决策变量。