Abstract

We present the first (to our knowledge) Deep-Learning based framework for real-time schedulability-analysis that guarantees to never incorrectly mis-classify an unschedulable system as being schedulable, and is hence suitable for use in safety-critical scenarios. We relate applicability of this framework to well-understood concepts in computational complexity theory: membership in the complexity class NP. We apply the framework upon the widely-studied schedulability analysis problems of determining whether a given constrained-deadline sporadic task system is schedulable on a preemptive uniprocessor under both Deadline-Monotonic and EDF scheduling. As a proof-of-concept, we implement our framework for Deadline-Monotonic scheduling, and demonstrate that it has a predictive accuracy exceeding 70% for systems of as many as 20 tasks without making any unsafe predictions. Furthermore, the implementation has very small (< 1 ms on two widely-used embedded platforms; < 4μs on an embedded FPGA) and highly predictable running times.

Department(s)

Computer Science

Publication Status

Open Access

Comments

Uppsala Universitet, Grant None

Recommended Citation: Baruah, S., Ekberg, P. & Sudvarg, M. Learning-assisted schedulability analysis: opportunities and limitations. Real-Time Syst 61, 332–358 (2025). https://doi.org/10.1007/s11241-025-09450-y

Keywords and Phrases

Computational complexity: NP-completeness; Deep learning; Learning-enabled components (LECs); Schedulability analysis

International Standard Serial Number (ISSN)

1573-1383; 0922-6443

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Springer, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Dec 2025

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