Application specific performance indicators for quantitative evaluation of the timing behavior for embedded real-time systems

König,; Boers,; Slomka,; Margull, Ulrich; Niemetz,; Wirrer,

doi:10.1109/date.2009.5090719

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…To configure a specific optimization run, configuration parameters have to be provided for each of the aforementioned steps. They are as follows: Per‐Solution Simulation Time: This is the timespan covered in the simulation for each created solution during optimization. Configuration of the Fitness Function: Several timing and performance metrics as introduced in section 3.3.2 are aggregated together into a scalar fitness value for each solution by using a modified euclidean norm . For each incorporated metric, a weight factor as well as a lower and upper limit for normalization has to be provided. Population Size: This parameter defines the number of solutions created in the initial population as well of the number of new solutions which are created during each iteration of the algorithm. Selection Size: The selection size is the amount of best solutions according to fitness which are taken over into the next iteration.…”

Section: Approach In Detailmentioning

confidence: 99%

“…• Configuration of the Fitness Function: Several timing and performance metrics as introduced in section 3.3.2 are aggregated together into a scalar fitness value for each solution by using a modified euclidean norm. 46,52 For each incorporated metric, a weight factor as well as a lower and upper limit for normalization has to be provided.…”

Section: Optimization Parametersmentioning

confidence: 99%

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Parallelizing highly complex engine management systems

Kienberger

Schmidhuber²,

Saad

et al. 2017

Concurrency and Computation

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Summary The automotive industry seeks to include more and more features in its vehicles. For this purpose, the necessary policy shift towards multi‐core technology is in full swing. To eventually exploit the extra processing power, there is much additional effort needed for coping with the tremendously increased complexity. This is largely due to the elaborate parallelization process that spans a vast search space. Consequently, there is a strong need for innovative methods and appropriate tools for the migration of legacy single‐core software. We use the results of a data dependency analysis performed on AUTOSAR system descriptions to determine advantageous partitions as well as initial task‐to‐core mappings. Afterwards, the extracted information serves as input for the simulation within a multi‐core timing tool suite. Here, the initial solution is evaluated with respect to proper scheduling and metrics like cross‐core communication rates, communication latencies, or core load distribution. A subsequent optimization process improves the initial solution and enables a comparative assessment. To demonstrate the benefit, we substantially expand a previous case study by applying our approach to two complex engine management systems and by showing the advantages compared to a parallelization process without preceding dependency analysis and initial partition/mapping suggestions.

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Section: Approach In Detailmentioning

confidence: 99%

Section: Optimization Parametersmentioning

confidence: 99%

Parallelizing highly complex engine management systems

Kienberger

Schmidhuber²,

Saad

et al. 2017

Concurrency and Computation

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“…Remark that during the design of an industrial application, WCET is rarely used to evaluate the schedulability of an application. Konig et al explain in [12] that engine control applications are not schedulable using classic methods with WCET, but the systems are still effective in practice. Although the WCET is useful to have a theoretical evaluation of the schedulability for hard real-time systems, these practical cases invite criticism about the use of WCET as parameter in scheduling algorithms and in the evaluation of their performance.…”

Section: A Worst-case Execution Timementioning

confidence: 99%

Simulation of real-time scheduling with various execution time models

Chéramy¹,

Hladik²,

Déplanche³

et al. 2014

Proceedings of the 9th IEEE International Symposium on Industrial Embedded Systems (SIES 2014)

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In this paper, we present SimSo, a simulator that aims at facilitating the design of experimental evaluations for real-time scheduling algorithms. Currently, more than twentyfive algorithms were implemented. Special attention is paid to the execution time model of tasks. We show that the worst-case execution time for experimental simulation can introduce a bias in evaluation and we discuss as a work in progress how cache effects could be taken into consideration in the simulation.

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“…Predicting real-time behavior of distributed automotive control systems is an enormous challenge [4]. For the analysis of real-time behavior of distributed control systems there exists a gap between analytical approaches like SymTA/S [8] and simulation environments like chronSIM [1].…”

Section: Co-designmentioning

confidence: 99%

Timing and schedulability analysis for distributed automotive control applications

Chakraborty¹,

Natale

Falk

et al. 2011

Proceedings of the Ninth ACM International Conference on Embedded Software

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High-end cars today consist of more than 100 electronic control units (ECUs) that are connected to a set of sensors and actuators and run multiple distributed control applications. The design flow of such architectures consists of specifying control applications as Simulink/Stateflow models, followed by generating code from them and finally mapping such code onto multiple ECUs. In addition, the scheduling policies and parameters on both the ECUs and the communication buses over which they communicate also need to be specified. These policies and parameters are computed from high-level timing and control performance constraints. The proposed tutorial will cover different aspects of this design flow, with a focus on timing and schedulability problems. After reviewing the basic concepts of worst-case execution time analysis and schedulability analysis, we will discuss the differences between meeting timing constraints (as in classical real-time systems) and meeting control performance constraints (e.g., stability, steady and transient state performance). We will then describe various control performance related schedulability analysis techniques and how they may be tied to model-based software development. Finally, we will discuss various schedule synthesis techniques, both for ECUs as well as for communication protocols like FlexRay, so that control performance constraints specified at the model-level may be satisfied. Throughout the tutorial different commercial as well as academic tools will be discussed and demonstrated. Copyright is held by the author/owner(s). EMSOFT'11, October 9-14, 2011, Taipei, Taiwan. ACM 978-1-4503-0714-7/11/10. Categories and Subject Descriptors Control Applications• control requirements (stability, settling time) • timing requirements (end-to-end latencies, WCETs)Automotive ArchitecturesFigure 1: Illustration of a concurrent design of distributed automotive control systems. OUTLINEIn the following, different aspects of a design flow for timing and schedulability analysis of distributed automotive control application are discussed. These aspects break down to state-of-the-art automotive architectures, control applications, and a design methodology that considers a concurrent co-design. An overview is illustrated in Figure 1. Automotive ArchitecturesModern cars consist of a large number of electronic components that carry out highly diverse applications with the goal to increase the safety and comfort of the driver. Recent innovations in the automotive domain like adaptive cruise control or lane, parking, and, traffic assistant systems are put into practice by integrated hardware/software solutions. As a result, modern high-end cars consist of more than 100 electronic control units (ECUs) that are connected to a set of sensors and actuators, running a multitude of distributed control applications. These automotive architectures are highly heterogeneous, consisting of many different computational and communication components. This growing complexity issues enormous challenges to...

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Application specific performance indicators for quantitative evaluation of the timing behavior for embedded real-time systems

Cited by 8 publications

References 4 publications

Parallelizing highly complex engine management systems

Parallelizing highly complex engine management systems

Simulation of real-time scheduling with various execution time models

Timing and schedulability analysis for distributed automotive control applications

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