The Linux Real-Time Task Model Extractor
LiME is a tool designed to dynamically extract real-time task models from Linux workloads. By observing the temporal behavior of real-time threads, LiME automatically maps their activity to well-established real-time task models, including:
- Sporadic and periodic tasks
- Arrival curves (upper and lower)
- Cumulative execution-time curves ($\mathrm{WCET}_n$)
- Self-suspension models (dynamic and segmented)
Unlike traditional approaches, LiME operates fully automatically on unmodified Linux kernels, requiring no prior expertise in real-time theory or Linux internals.
You may find a lot of information on the tool and its theoretical aspects in our paper published at RTAS'25
- LiME: The Linux Real-Time Task Model Extractor https://people.mpi-sws.org/~bbb/papers/pdf/rtas25-lime.pdf
Why LiME?
With the PREEMPT_RT patch now part of the mainline Linux kernel, Linux has solidified its role as a major platform for hosting real-time workloads in automotive, aerospace, robotics, and other critical domains. However, despite its widespread use, many Linux-based real-time applications lack formal modeling and analysis, largely due to the absence of automated system introspection tools.
LiME bridges this gap by enabling automated, in situ modeling of real-time tasks, making schedulability analysis easily accessible to non-experts. We encourage you to try LiME if you are interested in:
- Validating timing behavior,
- Analysing system characteristics,
- Debugging unexpected latencies,
- Continuosly monitoring real-time applications,
- Learning more about the relationship between the Linux internals and real-time scheduling theory,
- And more.
Current Status
🚧 This website is still under construction! 🚧
A draft version of the documentation is available for early access, and we are actively working on expanding the content.
We are working towards an open source release of LiME. If you're interested in accessing the code before the official release, please email bbb@mpi-sws.org.
Stay tuned for updates! 🎯