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Design optimization for real-time systems with sustainable schedulability analysis

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Abstract

The design of modern real-time systems not only needs to guarantee their timing correctness, but also involves other critical metrics such as control quality and energy consumption. As real-time systems become increasingly complex, there is an urgent need for efficient optimization techniques that can handle large-scale systems. However, the complexity of schedulability analysis often makes it difficult to be directly incorporated in standard optimization frameworks, and inefficient to be checked against a large number of candidate solutions. In this paper, we propose a novel optimization framework for the design of real-time systems. It leverages the sustainability of schedulability analysis that is applicable for a large class of real-time systems. It builds a counterexample-guided iterative procedure to efficiently learn from an unschedulable solution and rule out many similar ones. Compared to the state-of-the-art, the proposed framework may be ten times faster while providing solutions with the same quality. This work is a journal extension to the conference paper published at RTSS 2020, which adds new discussions for techniques that improve the algorithm scalability, as well as a set of new experiments to better evaluate the proposed framework.

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Notes

  1. For simplicity we call such a schedulability analysis sustainable, but it is termed as self-sustainable analysis in Baker and Baruah (2009), see a detailed discussion therein.

  2. The only known exception is the MILP solver CPLEX (International Business Machines Corporation xxxx), which provides an interface for building customized branching tree. But the price is that it can no longer run in parallel on multiple cores, which actually makes the whole procedure slower.

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Acknowledgements

This work is partially funded by NSF Grants No. 1812963 and No. 1837519.

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Authors and Affiliations

  1. Virginia Polytechnic Institute and State University Blacksburg, Virginia, USA

    Yecheng Zhao, Runzhi Zhou & Haibo Zeng

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  1. Yecheng Zhao
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  2. Runzhi Zhou
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  3. Haibo Zeng
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Correspondence to Yecheng Zhao.

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Zhao, Y., Zhou, R. & Zeng, H. Design optimization for real-time systems with sustainable schedulability analysis. Real-Time Syst 58, 275–312 (2022). https://doi.org/10.1007/s11241-022-09388-5

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