The Linux kernel offers nearly 20,000 configuration options, making it highly customizable but also extremely challenging to manually optimize for performance. The diversity of operating environments and workloads further limits the effectiveness of static or expert-crafted configurations. This paper introduces KconfigTune, an automated tuning system that jointly optimizes Linux kernel configuration options and system-level parameters (e.g., procfs entries). We propose extconfig to expand the parameter search space and, for the first time, address configuration dependencies that arise during automated tuning. To ensure valid and bootable kernels, we design a dependency automatic fix tool and a GRUB-based mechanism. KconfigTune models the tuning process as a machine learning problem and applies Bayesian optimization with a random forest model to efficiently explore the vast and interdependent configuration space. Unlike prior approaches that focus solely on either kernel options or system parameters, KconfigTune achieves deeper integration by jointly tuning both compile-time and runtime behaviors. Experimental results show significant performance improvements, with gains of 18.62% and 19.92% over the default configuration in UnixBench and LEBench tests, respectively. Compared to the state-of-the-art, KconfigTune outperforms by 2.9% and 12.69% in these benchmarks. Ablation studies further confirm that these gains primarily stem from the combined tuning of kernel configurations and system-level parameters.

Shared libraries are widely used in software development to execute third-party functions. However, the size and complexity of shared libraries tend to increase with the need to support more features, resulting in bloated shared libraries. This leads to resource waste and security issues as a significant amount of generic functionality is included unnecessarily in most scenarios, especially in embedded systems. To address this issue, previous works attempt to debloat shared libraries through binary rewriting or recompilation. However, these works face a tradeoff between flexibility in usage (needs recompilation and runtime support) and the effectiveness of debloating (binary rewriting achieves insufficient file size reduction). We propose D-Linker, a tool that debloats shared libraries by reducing both code and data sections in link-time at the object level without recompilation. Our key insight is that object-level shared library debloating is especially suitable for embedded systems because it strikes a balance of flexibility and efficiency. D-Linker identifies the required ELF object files of the shared libraries in an application and relinks them to produce a debloated shared library with better-debloating effectiveness by avoiding the data reference analysis. Our approach achieves over 70% of gadgets reduction as a security benefit and an average size reduction of 49.6% for a stripped libc of coreutils. The results also indicate that D-Linker improves debloating effectiveness by approximately 30% compared to binary-level shared library debloating and incurs a 5% decrease in code gadgets reduction compared to source-code-level shared library debloating.

Abstract The command line interface is a crucial way of interacting with Linux, many programs such as ls, pwd and netstat are used on it and it is also the primary way to access a server remotely. However, the command line interface is not user friendly and thus it is difficult to use; there are many programs and users do not know which one is appropriate for finishing their task. To help users find useful commands efficiently, the authors propose FindCmd that retrieves commands based on the local data and user familiarity with commands. Then the local command data are collected including user manual such as man , info and strings extracted from the binary ELF (executable and linkable format) file. Based on the characteristics of local data, an enhanced command retrieval framework is proposed. In addition, the authors marginally decreased the priority of familiar commands when retrieving commands since users tend to use command retrieval tool to find an unfamiliar command. To the best of our knowledge, this is the first local tool for personalised command retrieval. In the evaluation section, the authors compare FindCmd with retrieval tools apropos and howdoi ; our experimental results show that FindCmd outperforms the other two tools in retrieving commands. In addition, the experiments demonstrate the effectiveness of personalised search of FindCmd.