A Statistical Approach to Response-Time Analysis of Complex Embedded Real-Time Systems

Lu, Yue; Nolte, Thomas; Kraft, Johan; Norström, Christer

doi:10.1109/rtcsa.2010.13

Cited by 17 publications

(13 citation statements)

References 12 publications

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“…by consuming some energy (E). The probability of each task is calculated using the Probability Distribution Function (PDF) as in in [32] [33]. For example, when task is executed on the core under the voltage level , it will take 6T time with probability 1.0 and consume 7E energy.…”

Section: Motivational Examplementioning

confidence: 99%

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Energy-aware scheduling on heterogeneous multi-core systems with guaranteed probability

Liu

Niu

Atiquzzaman

et al. 2017

Journal of Parallel and Distributed Computing

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Section: Motivational Examplementioning

confidence: 99%

“…Proof: according to the Probability Distribution Function (PDF) introduced in [32] [33], we have that the probability is accumulated probability obtained by using statistical methods, which is defined as: = +…”

Section: Theorems Of Accelerated Search Algorithmmentioning

confidence: 99%

Energy-aware scheduling on heterogeneous multi-core systems with guaranteed probability

Liu

Niu

Atiquzzaman

et al. 2017

Journal of Parallel and Distributed Computing

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“…For example, when task ‫ݒ‬ ଷ is executed on the core ܿ ଵ under the voltage level ‫݈ݒ‬ ଵ , it will take 6T time with probability 1.0 and consume 7E energy. The probability of each task is calculated using the Probability Distribution Function (PDF) which can be obtained in [19].…”

Section: Motivational Examplementioning

confidence: 99%

Optimizing Tasks Assignment on Heterogeneous Multi-core Real-Time Systems with Minimum Energy

Niu

Qiu

et al. 2015

2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium

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The main challenge for embedded real-time systems, especially for mobile devices, is the trade-off between system performance and energy efficiency. Through studying the relationship between energy consumption, execution time and completion probability of tasks on heterogeneous multi-core architectures, we propose an Accelerated Search algorithm based on dynamic programming to obtain a combination of various task schemes which can be completed in a given time with a confidence probability by consuming the minimum possible energy. We adopt a DAG (Directed Acyclic Graph) to represent the precedent relation between tasks and develop a Minimum-Energy Model to find the optimal tasks assignment. The heterogeneous multi-core architectures can execute tasks under different voltage level with DVFS which leads to different execution time and different consumption energy. The experimental results demonstrate our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 24.6%).

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“…Simulation-based timing analysis methods have expanded both in terms of Response-Time Analysis (RTA) for more complex systems [13,5] and how the results are subsequently used, e.g., by analyzing the timing properties of the existing code and wrapping it into components, which facilitate migration towards a component-based real-time system. Simulation-based methods provide a powerful augmentation to RTA as they allow the user to analyze the impact of changes on a system's temporal behavior, before introducing changes to the system, which is referred to as timing impact analysis [2].…”

Section: Introductionmentioning

confidence: 99%

A statistical approach to simulation model validation in response-time analysis of complex real-time embedded systems

Kraft

Nolte

et al. 2011

Proceedings of the 2011 ACM Symposium on Applied Computing

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As simulation-based analysis methods make few restrictions on the system design and scale to very large and complex systems, they are widely used in, e.g., timing analysis of complex real-time embedded systems (CRTES) in industrial circles. However, before such methods are used, the analysis simulation models have to be validated in order to assess if they represent the actual system or not, which also matters to the confidence in the simulation results. This paper presents a statistical approach to validation of temporal simulation models extracted from CRTES, by introducing existing mature statistical hypothesis tests to the context. Moreover, our evaluation using simulation models depicting a fictive but representative industrial robotic control system indicates that the proposed method can successfully identify temporal differences between different simulation models, hence it has the potential to be considered as an effective simulation model validation technique.

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A Statistical Approach to Response-Time Analysis of Complex Embedded Real-Time Systems

Cited by 17 publications

References 12 publications

Energy-aware scheduling on heterogeneous multi-core systems with guaranteed probability

Energy-aware scheduling on heterogeneous multi-core systems with guaranteed probability

Optimizing Tasks Assignment on Heterogeneous Multi-core Real-Time Systems with Minimum Energy

A statistical approach to simulation model validation in response-time analysis of complex real-time embedded systems

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