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

2009

In this paper, we present a simple recursive equation to determine the best-case response times of periodic tasks under fixed-priority preemptive scheduling and arbitrary phasings. The approach is of a similar nature to the one used to determine worst-case response times, in the sense that, where a critical instant is considered to determine the latter, we base our analysis on an optimal instant, in which all higher priority tasks have a simultaneous release that coincides with the bestcase completion of the task under consideration. The resulting recursive equation closely resembles the one for worst-case response times, apart from a term ¢ 1 difference, and the fact that the bestcase response times are approached from above. The resulting iterative procedure is illustrated by means of a small example. Finally, we discuss the effect of the best-case response times on completion jitter, as well as the effect of release jitter on the best-case response times.

2004

Modern real-time systems, with a more flexible and adaptive nature, demand approaches for timeliness evaluation based on probabilistic measures of meeting deadlines. In this context, simulation can emerge as an adequate solution to understand and analyze the timing behaviour of actual systems. However, care must be taken with the obtained outputs under the penalty of obtaining results with lack of credibility. Particularly important is to consider that we are more interested in values from the tail of a probability distribution (near worst-case probabilities), instead of deriving confidence on mean values. We approach this subject by considering the random nature of simulation output data. We will start by discussing well known approaches for estimating distributions out of simulation output, and the confidence which can be applied to its mean values. This is the basis for a discussion on the applicability of such approaches to derive confidence on the tail of distributions, where the worst-case is expected to be.

Chapman & Hall/CRC Computer & Information Science Series, 2007

This chapter deals with the problem of how to estimate and analyze the execution time of embedded real-time software, in particular the worst-case execution time.

2009

In recent years, many component-based real-time systems have been proposed as a solution to modular and easily maintainable distributed real-time systems. This paper proposes a methodology for estimating probability distributions of execution times in the context of such systems, where no access to component internal code is assumed. In order to evaluate the proposed methodology, experiments were conducted with components, and related compositions, implemented over CIAO and ARCOS. CIAO is a known real-time component-based middleware and ARCOS is a software framework devoted to the construction of real-time control and supervision applications, also developed over CIAO. The collected experimental data show that the proposed approach is indeed a good approximation for component execution time probability distributions.

2007

This paper describes an algorithm to determine the performance of real-time systems with tasks using stochastic processing times. Such an algorithm can be used for guaranteeing Quality of Service of periodic tasks with soft real-time constraints. We use a discrete distribution model of processing times instead of worst case times like in hard real-time systems. Such a model gives a more realistic view on the actual requirements of the system. The presented algorithm works for all deterministic scheduling systems, which makes it more general than existing 6algorithms and allows us to compare performance between these systems. To demonstrate our method, we make a comparison between the performance of the well known scheduling algorithms Earliest Deadline First and Rate Monotonic. We show that the complexity of our method can compete with other algorithms that work for a wide range of schedulers.

2016 Euromicro Conference on Digital System Design (DSD), 2016

The use of increasingly complex hardware and software platforms in response to the ever rising performance demands of modern real-time systems complicates the verification and validation of their timing behaviour, which form a time-and-effort-intensive step of system qualification or certification. In this paper we relate the current state of practice in measurement-based timing analysis, the predominant choice for industrial developers, to the proceedings of the PROXIMA 1 project in that very field. We recall the difficulties that the shift towards more complex computing platforms causes in that regard. Then we discuss the probabilistic approach proposed by PROXIMA to overcome some of those limitations. We present the main principles behind the PROXIMA approach as well as the changes it requires at hardware or software level underneath the application. We also present the current status of the project against its overall goals, and highlight some of the principal confidence-building results achieved so far.

2009 Design, Automation & Test in Europe Conference & Exhibition, 2009

In the design and development of embedded realtime systems the aspect of timing behavior plays a central role. Especially, the evaluation of different scheduling approaches, algorithms and configurations is one of the elementary preconditions for creating not only reliable but also efficient systems-a key for success in industrial mass production. This is becoming even more important as multi-core systems are more and more penetrating the world of embedded systems together with the large (and growing) variety of scheduling policies available for such systems. In this work simple mathematical concepts are used to define performance indicators allowing to quantify the benefit of different solutions of the scheduling challenge for a given application. As a sample application some aspects of analyzing the dynamic behavior of an combustion engine management system for the automotive domain are shown. However, the described approach is flexible in order to support the specific optimization needs arising from the timing requirements defined by the application domain and can be used with simulation data as well as target system measurements.

Lecture Notes in Computer Science, 2002

The main contribution of this paper is accurate analysis of real-time performance for dynamic real-time applications. A wrong system performance analysis can lead to a catastrophe in a dynamic real-time system. In addition, real-time performance guarantee combined with efficient resource utilization is observed by experiments, while the previous worst-case approaches primarily focused on performance guarantee but resulted in typically poor utilization. The subsequent contribution is schedulability analysis for a feasible allocation of resource management on the Solaris operating system. This is accomplished with a mathematical model and by accurate response time prediction for a periodic, dynamic distributed real-time application.

Dependable Software Engineering. Theories, Tools, and Applications, 2017

Probabilistic approaches to timing analysis derive probability distributions to upper bound task execution time. The main purpose of probability distributions instead of deterministic bounds, is to have more flexible and less pessimistic worst-case models. However, in order to guarantee safe probabilistic worst-case models, every possible execution condition needs to be taken into account. In this work, we propose probabilistic representations which is able to model every task and system execution conditions, included the worstcases. Combining probabilities and multiple conditions offers a flexible and accurate representation that can be applied with mixed-critical task models and fault effect characterizations on task executions. A case study with single-and multi-core real-time systems is provided to illustrate the completeness and versatility of the representation framework we provide.