Response Time Analysis for Fixed-Priority Tasks with Multiple Probabilistic Parameters

Maxim, Dorin; Cucu-Grosjean, Liliana

doi:10.1109/rtss.2013.30

Cited by 52 publications

(77 citation statements)

References 19 publications

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“…The main problem with these methods is using the same probability distributions to model the execution times and inter-arrival times of all tasks is not realistic. In 2013 Maxim and Cucu [34] extended the probabilistic timing analysis of Diaz et al [21] to the case where tasks are allowed to have multiple sources of variability in their parameters, that is, not just the execution time is given as a random variable but the inter-arrival times and deadlines as well, the analysis being able to cope with multiple probabilistic parameters given as random variables and returns a probability distribution representing the possible response times of the task if it were to be instantiated at the same time as all higher priority tasks, i.e. the synchronous case.…”

Section: Probabilistic Timing Analysismentioning

confidence: 99%

“…It was shown in [34] that the case when tasks are simultaneously released yields the greatest response time distribution for each task respectively. Here, greatest is defined with respect to the relation and it indicates that the response time distribution of the first job upper bounds the response time distribution of any other job of that task.…”

Section: Definition 6 the Probabilistic Minimal Inter-arrival Time (mentioning

confidence: 99%

“…The technique presented in [34] is implemented in a probabilistic analysis tool and, in parallel to the simulator, is used to calculate the probabilistic WCRT (pWCRT), Ri, of each generated task. The response time distribution thus computed is used as a ground truth in our experiments.…”

Section: Probabilistic Analysis Toolmentioning

confidence: 99%

“…For example, the analysis can be terminated once an accuracy of 10 −9 has been reached for the response time. Further details about the Probabilistic Analysis Tool can be found in [34].…”

Section: Probabilistic Analysis Toolmentioning

confidence: 99%

“…The application of EVA-based techniques to the WCET analysis problem was extended to combine measurements with static analysis to form hybrid analysis [8] and the use of copulas to allow statistical analyses results to account for possible dependencies between different behaviours of the system [9]. Statistical analysis techniques, other than fitting a Gumbel distribution, have been applied to improve the results obtained including block maxima-based approaches [17,26] and theoretical analyses [34]. In addition to WCET, EVA-based techniques have been applied in other areas of real-time systems research including for Worst-Case Response Times (WCRT) of tasks Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

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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: Probabilistic Timing Analysismentioning

confidence: 99%

Section: Definition 6 the Probabilistic Minimal Inter-arrival Time (mentioning

confidence: 99%

Section: Probabilistic Analysis Toolmentioning

confidence: 99%

“…For example, the analysis can be terminated once an accuracy of 10 −9 has been reached for the response time. Further details about the Probabilistic Analysis Tool can be found in [34].…”

Section: Probabilistic Analysis Toolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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 response‐time analysis for real‐time systems in body area sensor networks

Ren

et al. 2015

Int J Communication

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SUMMARYAdvances in real-time system and wireless communication have led to the deployment of body area sensor networks (BASNs) for effective real-time healthcare applications. Real-time systems in BASNs tend increasingly to be probabilistic and mixed critical to meet stringent requirements on space, weight, and power consumption. Response-time analysis is an important and challenging task for BASNs to provide some critical services. In this paper, we propose a request-based compositional probabilistic response-time analysis framework for probabilistic real-time task models with fixed-priority preemptive scheduling in BASNs. In this method, each probabilistic real-time task is abstracted as a probabilistic request function. Rough response-time distribution is computed first based on the cumulative request distribution and then exact response-time distribution is obtained by refinement based on the request increase distribution. Our strategy can effectively improve performance by reducing repetitive computational overhead for the probabilistic response-time analysis of all tasks in the system. Our evaluation demonstrates that our proposed method significantly outperforms the existing probabilistic response-time analysis algorithm in terms of analysis duration.

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The PROSIT tool: Toward the optimal design of probabilistic soft real‐time systems

et al. 2018

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Summary In recent years, series of important achievements have paved the way for the introduction of probabilistic analysis in the area of soft real‐time systems design. In this article, we present an extensible design tool, called PROSIT, which facilitates the access to this technology for a potentially large number of researchers and industrial practitioners. The tool enables the probabilistic analysis of the temporal performance of a real‐time task under fixed‐priority and resource reservations scheduling algorithms. For resource reservations, the tool also offers an automatic procedure for the synthesis of scheduling parameters that optimize a quality metric related to the probabilistic behavior of the tasks.

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Response Time Analysis for Fixed-Priority Tasks with Multiple Probabilistic Parameters

Cited by 52 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 response‐time analysis for real‐time systems in body area sensor networks

The PROSIT tool: Toward the optimal design of probabilistic soft real‐time systems

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