Optimization of Static Task and Bus Access Schedules for Time-Triggered Distributed Embedded Systems with Model-Checking

Using Timed Automata for Modeling the Clocks of Distributed Embedded Systems

Behavioral Modeling for Embedded Systems and Technologies ◽

10.4018/978-1-60566-750-8.ch007 ◽

2010 ◽

pp. 172-193 ◽

Cited By ~ 1

Author(s):

Guillermo Rodriguez-Navas ◽

Julian Proenza ◽

Hans Hansson ◽

Paul Pettersson

Keyword(s):

Model Checking ◽

Embedded Systems ◽

Embedded System ◽

Timed Automata ◽

Model Checker ◽

Distributed Embedded Systems ◽

Temporal Behaviour ◽

System Designer ◽

Functional Aspects ◽

Time Systems

Model checking is a widely used technique for the formal verification of computer systems. However, the suitability of model checking strongly depends on the capacity of the system designer to specify a model that captures the real behaviour of the system under verification. For the case of real-time systems, this means being able to realistically specify not only the functional aspects, but also the temporal behaviour of the system. This chapter is dedicated to modeling clocks in distributed embedded systems using the timed automata formalism. The different types of computer clocks that may be used in a distributed embedded system and their effects on the temporal behaviour of the system are introduced, together with a systematic presentation of how the behaviour of each kind of clock can be modeled. The modeling is particularized for the UPPAAL model checker, although it can be easily adapted to other model checkers based on the theory of timed automata.

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Optimization of static task and bus access schedules for time-triggered distributed embedded systems with model-checking

2007 44th ACM/IEEE Design Automation Conference ◽

10.1145/1278480.1278556 ◽

2007 ◽

Cited By ~ 6

Author(s):

Zonghua Gu ◽

Xiuqiang He ◽

Mingxuan Yuan

Keyword(s):

Model Checking ◽

Embedded Systems ◽

Distributed Embedded Systems

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Sensitivity-driven co-synthesis of distributed embedded systems

Proceedings of the 8th international symposium on System synthesis - ISSS '95 ◽

10.1145/224486.224488 ◽

1995 ◽

Cited By ~ 4

Author(s):

Ti-Yen Yen ◽

Wayne Wolf

Keyword(s):

Embedded Systems ◽

Distributed Embedded Systems

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MODES: model-based optimization on distributed embedded systems

Machine Learning ◽

10.1007/s10994-021-06014-6 ◽

2021 ◽

Author(s):

Junjie Shi ◽

Jiang Bian ◽

Jakob Richter ◽

Kuan-Hsun Chen ◽

Jörg Rahnenführer ◽

...

Keyword(s):

Machine Learning ◽

Embedded Systems ◽

Learning Model ◽

Black Box ◽

Distributed Embedded Systems ◽

Data Set ◽

Individual Model ◽

Model Based ◽

Machine Learning Model ◽

Distributed Machine Learning

AbstractThe predictive performance of a machine learning model highly depends on the corresponding hyper-parameter setting. Hence, hyper-parameter tuning is often indispensable. Normally such tuning requires the dedicated machine learning model to be trained and evaluated on centralized data to obtain a performance estimate. However, in a distributed machine learning scenario, it is not always possible to collect all the data from all nodes due to privacy concerns or storage limitations. Moreover, if data has to be transferred through low bandwidth connections it reduces the time available for tuning. Model-Based Optimization (MBO) is one state-of-the-art method for tuning hyper-parameters but the application on distributed machine learning models or federated learning lacks research. This work proposes a framework $$\textit{MODES}$$ MODES that allows to deploy MBO on resource-constrained distributed embedded systems. Each node trains an individual model based on its local data. The goal is to optimize the combined prediction accuracy. The presented framework offers two optimization modes: (1) $$\textit{MODES}$$ MODES -B considers the whole ensemble as a single black box and optimizes the hyper-parameters of each individual model jointly, and (2) $$\textit{MODES}$$ MODES -I considers all models as clones of the same black box which allows it to efficiently parallelize the optimization in a distributed setting. We evaluate $$\textit{MODES}$$ MODES by conducting experiments on the optimization for the hyper-parameters of a random forest and a multi-layer perceptron. The experimental results demonstrate that, with an improvement in terms of mean accuracy ($$\textit{MODES}$$ MODES -B), run-time efficiency ($$\textit{MODES}$$ MODES -I), and statistical stability for both modes, $$\textit{MODES}$$ MODES outperforms the baseline, i.e., carry out tuning with MBO on each node individually with its local sub-data set.

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