Linux Kernel Configurations at Scale: A Dataset for Performance and Evolution Analysis

TL;DR

This paper introduces LinuxData, a large-scale dataset of Linux kernel configurations across multiple versions, enabling advanced research in kernel configuration analysis, prediction, and evolution modeling.

Contribution

The paper provides the first comprehensive, publicly accessible dataset of Linux kernel configurations with detailed measurements, supporting machine learning and transfer learning research.

Findings

Dataset includes over 240,000 configurations from versions 4.13 to 5.8.

Enables research in feature selection and prediction models.

Facilitates reproducibility and new insights into kernel configuration evolution.

Abstract

Configuring the Linux kernel to meet specific requirements, such as binary size, is highly challenging due to its immense complexity-with over 15,000 interdependent options evolving rapidly across different versions. Although several studies have explored sampling strategies and machine learning methods to understand and predict the impact of configuration options, the literature still lacks a comprehensive and large-scale dataset encompassing multiple kernel versions along with detailed quantitative measurements. To bridge this gap, we introduce LinuxData, an accessible collection of kernel configurations spanning several kernel releases, specifically from versions 4.13 to 5.8. This dataset, gathered through automated tools and build processes, comprises over 240,000 kernel configurations systematically labeled with compilation outcomes and binary sizes. By providing detailed records of configuration evolution and capturing the intricate interplay among kernel options, our dataset enables innovative research in feature subset selection, prediction models based on machine learning, and transfer learning across kernel versions. Throughout this paper, we describe how the dataset has been made easily accessible via OpenML and illustrate how it can be leveraged using only a few lines of Python code to evaluate AI-based techniques, such as supervised machine learning. We anticipate that this dataset will significantly enhance reproducibility and foster new insights into configuration-space analysis at a scale that presents unique opportunities and inherent challenges, thereby advancing our understanding of the Linux kernel's configurability and evolution.