Run-time monitoring of real-time systems

Chodrow, Sarah E.; Jahanian, Farnam; Donner, Marc

doi:10.1109/real.1991.160360

Cited by 75 publications

(47 citation statements)

References 8 publications

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“…Experimental results are crucial in the development of software systems and schedulers are no exceptions (Chodrow et al 1991). Indeed, there are many tools that aim at testing and verifying different aspects of schedulers for real-time systems.…”

Section: Related Workmentioning

confidence: 99%

rt-muse: measuring real-time characteristics of execution platforms

2017

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Operating systems code is often developed according to principles like simplicity, low overhead, and low memory footprint. Schedulers are no exceptions. A scheduler is usually developed with flexibility in mind, and this restricts the ability to provide real-time guarantees. Moreover, even when schedulers can provide realtime guarantees, it is unlikely that these guarantees are properly quantified using theoretical analysis that carries on to the implementation. To be able to analyze the guarantees offered by operating systems' schedulers, we developed a publicly available tool that analyzes timing properties extracted from the execution of a set of threads and computes the lower and upper bounds to the supply function offered by the execution platform, together with information about migrations and statistics on execution times. rt-muse evaluates the impact of many application and platform characteristics including the scheduling algorithm, the amount of available resources, the usage of shared resources, and the memory access overhead. Using rt-muse, we show the impact of Linux scheduling classes, shared data and application parallelism, on the delivered computing capacity. The tool provides useful insights on the runtime behavior of the applications and scheduler. In the reported experiments, rt-muse detected some issues arising with the real-time

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Section: Related Workmentioning

confidence: 99%

rt-muse: measuring real-time characteristics of execution platforms

2017

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“…In fact, conditions that may cause invalidation of the analysis results at runtime can occur. A typical example of this is the differences between the ideal execution environment (considered for performing analysis) and the actual one which can lead to the violation of the assumptions taken into account when performing static analysis (Chodrow et al 1991). On the other hand, the information gathered through monitoring system execution is useful for (i) observing the actual system's behavior and to detect violations at runtime, and for (ii) making adaptation decisions.…”

Section: Related Workmentioning

confidence: 99%

Architecture optimization: speed or accuracy? both!

et al. 2016

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Embedded systems are becoming more and more complex, thus demanding innovative means to tame their challenging development. Among others, early architecture optimization represents a crucial activity in the development of embedded systems to maximise the usage of their limited resources and to respect their real-time requirements. Typically, architecture optimization seeks good architecture candidates based on model-based analysis. Leveraging abstractions and estimates, this analysis usually produces approximations useful for comparing architecture candidates. Nonetheless, approximations do not provide enough accuracy in estimating crucial extra-functional properties. In this article, we provide an architecture optimization framework that profits from both the speed of model-based predictions and the accuracy of execution-based measurements. Model-based optimization rapidly finds a good architecture candidate, which is refined through optimization based on monitored executions of automatically generated code. Moreover, the framework enables the developer to leverage her optimization experience. More specifically, the developer can use runtime monitoring of generated code execution to manually adjust task allocation at modeling level, and commit the changes without halting execution. In the article, our architecture optimization mechanism is first described from a general point of view and then exploited for optimizing the allocation of software tasks to the processing cores of a multicore embedded system; we target extra-functional properties that can be

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“…Run-time monitoring of the consumed resources is intrinsic to realizing correct implementation of the scheduling and enforcement rules. Monitoring of real-time systems can be classified as synchronous or asynchronous (Chodrow et al, 1991). In the synchronous case, a constraint (e.g worst-case execution time) is examined by the task itself.…”

Section: Hierarchical Scheduling Frameworkmentioning

confidence: 99%