Re-sampling for statistical timing analysis of real-time systems

Maxim, Dorin; Houston, Michael J.; Santinelli, Luca; Bernat, Guillem; Davis, Robert I.; Cucu-Grosjean, Liliana

doi:10.1145/2392987.2393001

Cited by 31 publications

(23 citation statements)

References 19 publications

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“…task on priority level 5 in this case. Five tasks per task set and five values per random variable is enough to have significant results without the need to use approximation techniques such as re-sampling [35] to speed up the probabilistic analysis. Indeed, the probabilistic analysis is highly time consuming and a way to make it tractable is to use techniques such as re-sampling, but this kind of techniques introduces inaccuracy in the obtained results, and we are interested in having an exact theoretical response time distribution that matches the empirical distribution of response times observed during simulation.…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…The analysis is proven to be bounded in time and exact for both cases when the system utilisation is lower or greater than one. Due to the resource costs (computation time and memory) of convolution, the proposed analysis can only be applied for small task systems -this problem was later studied in [40] and [35] with the introduction and refinement, respectively, of resampling techniques meant to decrease the size of the distributions that are convolved while introducing minimal pessimism.…”

Section: Probabilistic Timing Analysismentioning

confidence: 99%

See 1 more Smart Citation

Study of the reliability of statistical timing analysis for real-time systems

Maxim¹,

Soboczenski

Bate

et al. 2015

Proceedings of the 23rd International Conference on Real Time and Networks Systems

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Probabilistic and statistical temporal analyses have been developedas a means of determining the worst-case execution and responsetimes of real-time software for decades. A number of such methodshave been proposed in the literature, of which the majority claim tobe able to provide worst-case timing scenarios with respect to agiven likelihood of a certain value being exceeded. Further, suchclaims are based on either some estimates associated with a probability,or probability distributions with a certain level of confidence.However, the validity of the claims are very much dependent on anumber of factors, such as the achieved samples and the adopteddistributions for analysis.In this paper, we investigate whether the claims made are in facttrue as well as the establishing an understanding of the factors thataffect the validity of these claims. The results are of importancefor two reasons: to allow researchers to examine whether there areimportant issues that mean their techniques need to be refined; andso that practitioners, including industrialists who are currently usingcommercial timing analysis tools based on these types of techniques,understand how the techniques should be used to ensure theresults are fit for their purposes. Study of the Reliability of Statistical Timing Analysis forReal-Time Systems ABSTRACTProbabilistic and statistical temporal analyses have been developed as a means of determining the worst-case execution and response times of real-time software for decades. A number of such methods have been proposed in the literature, of which the majority claim to be able to provide worst-case timing scenarios with respect to a given likelihood of a certain value being exceeded. Further, such claims are based on either some estimates associated with a probability, or probability distributions with a certain level of confidence. However, the validity of the claims are very much dependent on a number of factors, such as the achieved samples and the adopted distributions for analysis. This paper is the first one that puts side by side existing state of the art statistical and probabilistic analysis techniques, using the probabilistic analysis as the ground truth in order to asses the applicability and performance of the statistical technique. The evaluation clearly shows that for the experiments performed the approach can identify clear differences between a range of techniques and that these differences can be considered valid based on the trends expected from the academic theory.

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Section: Evaluation Resultsmentioning

confidence: 99%

Section: Probabilistic Timing Analysismentioning

confidence: 99%

Study of the reliability of statistical timing analysis for real-time systems

Maxim¹,

Soboczenski

Bate

et al. 2015

Proceedings of the 23rd International Conference on Real Time and Networks Systems

Self Cite

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“…An example of such schedulability thresholds is presented in Table III which also presents the degradation of tasks' failure thresholds as the criticality of the system increases. For example, a task of criticality 1 is allowed to have a DMP of 0.1 in system Table III PERMITTED DEADLINE MISS PROBABILITY THRESHOLDS FOR THE TASK A note on complexity: it is well known that probabilistic analyses are computationally intensive [11] and the analysis we propose in this paper makes no exception. Nevertheless there are efficient solutions in the literature to go around this problem, such as re-sampling [11].…”

Section: B Pmc Sufficient Schedulability Testmentioning

confidence: 99%

“…For example, a task of criticality 1 is allowed to have a DMP of 0.1 in system Table III PERMITTED DEADLINE MISS PROBABILITY THRESHOLDS FOR THE TASK A note on complexity: it is well known that probabilistic analyses are computationally intensive [11] and the analysis we propose in this paper makes no exception. Nevertheless there are efficient solutions in the literature to go around this problem, such as re-sampling [11]. We do not go into details about complexity and ways of reducing it, as we rather use simple task-sets to exemplify our technique and provide a proof of concept.…”

Section: B Pmc Sufficient Schedulability Testmentioning

confidence: 99%

Probabilistic schedulability analysis for fixed priority mixed criticality real-time systems

Abdeddaïm¹,

Maxim

2017

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2017

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“…In [25] the authors compare three different approaches for re-sampling discrete probability distributions characterizing task execution times. Re-sampling is used to combat the complexity when performing exact schedulability analysis on probability distributions.…”

Section: Related Workmentioning

confidence: 99%

Flexible Framework for Statistical Schedulability Analysis of Probabilistic Sporadic Tasks

Boudjadar

Kim

David

et al. 2015

2015 IEEE 18th International Symposium on Real-Time Distributed Computing

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Abstract-The analysis of probabilistic schedulability explores all possible combinations of the probabilities of task attributes, which can easily lead to exponential computation time [24]. In this paper, we present a flexible schedulability analysis framework for periodic and sporadic tasks having probabilistic attributes where the computation time scales linearly in the size of analyzed systems. The framework is given in terms of a set of Parameterized Stopwatch Automata (PSA) models, which leads to a large degree of flexibility. Probability distributions for response time are generated using statistical model checking (UPPAAL SMC) while the overall schedulability can be checked using symbolic model checking (UPPAAL). We also define PoMD (percentage of missed deadlines) as a measure of the probabilistic schedulability of systems. To evaluate our approach, we compare the time used for computing response times and the analysis results using similar task models to that of a related analytical approach.

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Re-sampling for statistical timing analysis of real-time systems

Cited by 31 publications

References 19 publications

Study of the reliability of statistical timing analysis for real-time systems

Study of the reliability of statistical timing analysis for real-time systems

Probabilistic schedulability analysis for fixed priority mixed criticality real-time systems

Flexible Framework for Statistical Schedulability Analysis of Probabilistic Sporadic Tasks

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