Probabilistic performance guarantee for real-time tasks with varying computation times

Tia, Too-Seng; Zhong, Dan; Shankar, Mallikarjun; Storch, M.; Sun, Jun; Wu, L.-C.; Liu, J.W.-S.

doi:10.1109/rttas.1995.516213

Cited by 161 publications

(98 citation statements)

References 10 publications

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“…Statistical characterizations were used by the authors of this paper in previous studies [18,36] and also by other authors [2,13,35]. The novelty of this paper is in properly modeling highly variable tasks using heavy-tailed distributions and extending task characterization with the inclusion of the Hurst parameter.…”

Section: System Modelmentioning

confidence: 98%

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On the nature and impact of self-similarity in real-time systems

Hernández-Orallo

Vila-Carbó

2012

Real-Time Syst

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On the nature and impact of self-similarity in real-time systems Enrique Hernández-Orallo and Joan Vila-Carbó the date of receipt and acceptance should be inserted later Abstract In real-time systems with highly variable task execution times simplistic task models are insufficient to accurately model and to analyze the system. Variability can be tackled using distributions rather than a single value, but the proper characterization depends on the degree of variability. Self-similarity is one of the deepest kinds of variability. It characterizes the fact that a workload is not only highly variable, but it is also bursty on many time-scales. This paper identifies in which situations this source of indeterminism can appear in a real-time system: the combination of variability in task inter-arrival times and execution times. Although self-similarity is not a claim for all systems with variable execution times, it is not unusual in some applications with real-time requirements, like video processing, networking and gaming.The paper shows how to properly model and to analyze self-similar task sets and how improper modeling can mask deadline misses. The paper derives an analytical expression for the dependence of the deadline miss ratio on the degree of self-similarity and proofs its negative impact on real-time systems performance through system's modeling and simulation. This study about the nature and impact of self-similarity on soft real-time systems can help to reduce its effects, to choose the proper scheduling policies, and to avoid its causes at system design time.

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Section: System Modelmentioning

confidence: 98%

“…Several statistical characterizations and analysis methods have been proposed [2,10,13,32,35,36] but they have not taken into account the selfsimilar nature of media workloads. Nevertheless, it is important to note the strong impact of variability on soft real-time scheduling in the experiments of [32].…”

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confidence: 99%

On the nature and impact of self-similarity in real-time systems

Hernández-Orallo

Vila-Carbó

2012

Real-Time Syst

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“…the task's WCET, Minimum Inter-arrival Time (MIT)) by using random variables. For WCET analysis multiple avenues have been explored, starting in 1995 when [41] introduced an analysis for tasks that have periodic releases but variable execution requirements. The algorithm, called Probabilistic Time Demand Analysis (PTDA) computes the worst case probability that a task misses its deadline by bounding the total amount of processor time demanded by all higher priority tasks.…”

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

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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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“…the task's WCET, Minimum Inter-arrival Time (MIT)). For WCET analysis multiple paths have been explored, starting in 1995 when [4] introduced an analysis for tasks that have periodic releases but variable execution requirements. The algorithm called Probabilistic Time Demand Analysis (PTDA) is based on a bound of the processor demand of higher priority tasks and hence it is highly pessimistic.…”

Section: Introductionmentioning

confidence: 99%