Analyzing execution path non-determinism of the Linux kernel in different scenarios

Abstract

Safety-critical systems play a significant role in industrial domains, and their complexity is increasing with advanced technologies such as Artificial Intelligence (AI). To provide efficient services, safety-critical systems that integrate AI applications are always built based on Linux, where Linux offers massive amounts of features and an incredibly perfect software ecosystem for AI applications. Since Linux is a pre-existing complex software system, different research programmes aim to pave the way for developing Linux-based safety-critical systems. Still, only some focus on the system calls for file operations. However, the execution path of a system call is effectively non-deterministic in Linux kernel space, which challenges the test coverage-based verification recommended by the functional safety standards. This research analyzes the influence of system state on Linux kernel path variability from two perspectives: file system type and system load. Therefore, an online data collection system for system call execution paths was constructed based on Ftrace, network file system (NFS), and MD5 hash function, uniquely identifying the system call execution path. The collected data were processed and analysed in this study. Evaluations show that the number of function execution paths of the system calls relevant to file systems increased with the increase in system load but would eventually be stable. Additionally, the function execution paths of the system call varied in different file systems. Based on the evaluations, the results of this work can provide advice for analyzing Linux-based safety-critical systems. In addition, the method introduced in this research can also provide support for the verification of Linux-based safety-critical systems.

Keywords:

• Linux kernel
• system call
• path variability
• non-determinism

Acknowledgements

The authors would like to thank Nicholas Mc Guire and Imanol Allende for their thoughtful advice and review.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was partially supported by National Key R&D Program of China under Grant No. 2020YFC0832500, Ministry of Education–China Mobile Research Foundation under Grant No. MCM20170206, the Fundamental Research Funds for the Central Universities under Grant No. lzujbky-2022-kb12, lzujbky-2021-sp43, National Natural Science Foundation of China under Grant No. 61402210, Google Research Awards and Google Faculty Award. This work was also partially supported by the Provincial Science and Technology Plan (Major Science and Technology Projects–Open Solicitation) (22ZD6GA048).