QUICompare VirtualBox Type-2 Hypervisor Dataset: Auto-Research Benchmarking of 15 TCP Congestion-Control Algorithms across Apache2, NGINX HTTP/2 and QUIC

Abstract

This dataset contains reproducible network-performance experiments generated with QUICompare Auto-Research in an Oracle VirtualBox Type-2 hypervisor environment. The infrastructure consists of two virtual machines: a QUICompare VM, responsible for workload generation and measurements, and a Device Under Test (DUT) VM The DUT was configured with 5 vCPUs while its virtual memory was varied between 1 GB and 4 GB vRAM. The QUICompare benchmarking tool obey the RFC 2544/6815. For each memory configuration, experiments were executed with 10, 50, and 100 concurrent users, producing six principal workload/resource scenarios: 1 GB × 10 users; 1 GB × 50 users; 1 GB × 100 users; 4 GB × 10 users; 4 GB × 50 users; and 4 GB × 100 users. The dataset evaluates 15 Linux TCP congestion-control algorithms under equivalent virtualized conditions, varying Web-server and protocol configurations based on Apache2, NGINX HTTP/2, and NGINX QUIC/HTTP/3. TCP congestion-control algorithms are directly applied to TCP-based Apache2 and NGINX HTTP/2 scenarios. QUIC/HTTP/3 is analyzed as a separate comparative branch because QUIC operates over UDP and implements congestion control independently from the Linux TCP stack. Experiments were automated using QUICompare Auto-Research mode, with FFmpeg-based traffic generation and configurable acquisition density through the `--sample-rate` parameter. The workflow systematically varies congestion-control algorithm, service/protocol, concurrent users, DUT vRAM, and experiment repetition. The experimental matrix can be summarized as: Congestion Control × Service/Protocol × Users × vRAM × Repetition The resulting files support analysis of throughput, transferred data, latency, jitter, packet behavior, TCP/UDP characteristics, and temporal network-performance series. When the corresponding QUICompare modes are enabled, the data can also support studies involving long-range dependence, self-similarity, fractal behavior, heavy-tailed traffic, Hurst-related measures, sensitivity analysis, machine learning, and Data Envelopment Analysis (DEA). The dataset was designed for reproducibility and controlled comparison of congestion-control mechanisms, Web protocols, workload intensity, and virtualized computational resources. Tutorials, research articles, software, preconfigured virtual machines, installation instructions, datasets, and reproducibility resources are available at the official QUICompare website: https://lhraphael.github.io/quicompare/quicompare.html Our more recent FRANCISCO DEA network predictor/comparison tool is available at URL: https://israellmt.shinyapps.io/Francisco-3-0/