Precise and Adaptable Worst-Case Execution Time Estimation in Hard Real-Time Systems

Abstract : Nowadays real-time systems are omnipresent and embedded systems thrive in a variety of application fields. When they are integrated into safety-critical systems, the verification of their properties becomes a crucial part. Dependability is a primary design goal in environments that use hard real-time systems, whereas general-use microprocessors were designed with a high performance goal. The average-throughput maximization design choice is intrinsically opposed to design goals such as dependability that benefit mostly from highly deterministic architectures without local optimizations. Besides the growth in complexity of the embedded systems, platforms are getting more and more heterogeneous. With regard to the respect of the timing constraints, real-time systems are classified in two categories: hard real-time systems (the non respect of a deadline can lead to catastrophic consequences) and soft real-time systems (missing a deadline can cause performance degradation and material loss). We analyze hard real-time systems that need precise and safe determination of the worst-case execution time bounds in order to be certified. The validation of their non-functional properties is a complex and resource consuming task. One of the main reasons is that currently available solutions focus on delivering precise estimations through tools that are highly dependent on the underlying platform (in order to provide precise and safe results, the architecture of the system must be taken into account). In this thesis we address the above issues by introducing a timing analysis method that maintains a good level of precision while being applicable to a variety of platforms. This adaptability is achieved through separating as much as possible the worst-case execution time (WCET) estimation from the model of the hardware. Our approach consists in the introduction of a new formal modeling language that captures the complex behaviour of modern hardware and is guided by the timing analysis in order to achieve the needed precision to scalability tradeoff. The analysis drives a conjoint symbolic execution of the program's binary and the processor model using a dynamic prediction module that decides what states to merge in order to limit the state space explosion. Several state merging algorithms are introduced and applied that can also give an estimation of the introduced precision loss.
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Vladimir-Alexandru Paun. Precise and Adaptable Worst-Case Execution Time Estimation in Hard Real-Time Systems. Computation and Language [cs.CL]. Ecole Doctorale Polytechnique, 2014. English. ⟨tel-01214985v1⟩

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