LiME: the Linux real-time task model extractor
LiME: the Linux real-time task model extractor
We present LIME, a novel dynamic real-time task model extractor. LIME observes the temporal behavior of Linux real-time threads and automatically maps the observed activity to established real-time task models: sporadic and periodic tasks, upper and lower arrival curves, cumulative execution-time curves, and two self-suspension models (dynamic and segmented). LIME runs on unmodified Linux kernels and requires neither knowledge of real-time theory nor familiarity with Linux internals to be used effectively. An extensive evaluation shows LIME to achieve very high inference accuracy—in particular 100% accuracy for common automotive periods—with low kernel overhead, low latency impact, and low processor utilization (at best-effort priority).
ebpf, event-arrival curve, linux, measurement-based parameter estimation, model extraction, model inference, periodic tasks, real-time task models, self-suspensions, sporadic tasks, tracing
255-269
Brandenburg, Björn B.
d78375e2-2ee4-4923-96f8-75606d9d0e7e
Courtaud, Cédric
7bc4c6ad-d388-4e56-aa3d-3ea502705ae5
Markovic, Filip
d0b77f7a-3b33-47d0-aaf1-9ab08823a372
Ye, Bite
67cddec7-10c1-49e6-875e-fcbdcb1c4ee9
6 June 2025
Brandenburg, Björn B.
d78375e2-2ee4-4923-96f8-75606d9d0e7e
Courtaud, Cédric
7bc4c6ad-d388-4e56-aa3d-3ea502705ae5
Markovic, Filip
d0b77f7a-3b33-47d0-aaf1-9ab08823a372
Ye, Bite
67cddec7-10c1-49e6-875e-fcbdcb1c4ee9
Brandenburg, Björn B., Courtaud, Cédric, Markovic, Filip and Ye, Bite
(2025)
LiME: the Linux real-time task model extractor.
In Proceedings - 31st IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2025.
IEEE.
.
(doi:10.1109/RTAS65571.2025.00033).
Record type:
Conference or Workshop Item
(Paper)
Abstract
We present LIME, a novel dynamic real-time task model extractor. LIME observes the temporal behavior of Linux real-time threads and automatically maps the observed activity to established real-time task models: sporadic and periodic tasks, upper and lower arrival curves, cumulative execution-time curves, and two self-suspension models (dynamic and segmented). LIME runs on unmodified Linux kernels and requires neither knowledge of real-time theory nor familiarity with Linux internals to be used effectively. An extensive evaluation shows LIME to achieve very high inference accuracy—in particular 100% accuracy for common automotive periods—with low kernel overhead, low latency impact, and low processor utilization (at best-effort priority).
Text
rtas25-lime
- Accepted Manuscript
More information
Published date: 6 June 2025
Venue - Dates:
IEEE 31st Real-Time and Embedded Technology and Applications Symposium (RTAS), , Irvine, United States, 2025-05-06 - 2025-05-09
Keywords:
ebpf, event-arrival curve, linux, measurement-based parameter estimation, model extraction, model inference, periodic tasks, real-time task models, self-suspensions, sporadic tasks, tracing
Identifiers
Local EPrints ID: 503340
URI: http://eprints.soton.ac.uk/id/eprint/503340
ISSN: 1545-3421
PURE UUID: 447b79b9-e240-44ef-9874-2703760e485f
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Date deposited: 29 Jul 2025 16:48
Last modified: 21 Aug 2025 05:12
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Contributors
Author:
Björn B. Brandenburg
Author:
Cédric Courtaud
Author:
Filip Markovic
Author:
Bite Ye
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