Reconfigurable Communication Middleware for Flex Ray-Based Distributed Embedded Systems

2015 ◽  
Author(s):  
Diptesh Majumdar ◽  
Licong Zhang ◽  
Purandar Bhaduri ◽  
Samarjit Chakraborty
Keyword(s):  
Embedded Systems ◽  
Distributed Embedded Systems ◽  
Communication Middleware

Building Component Based Software Connectors for Communication Middleware in Distributed Embedded Systems

2007 ◽  
Author(s):  
Dietmar Schreiner ◽  
Karl M. Go¨schka
Keyword(s):  
Embedded Systems ◽  
Good Practice ◽  
Building Blocks ◽  
Automatic Generation ◽  
Distributed Embedded Systems ◽  
Model Driven ◽  
Communication Middleware ◽  
Architectural Models ◽  
Sound Foundation ◽  
Software Connectors

Interaction in distributed component based software-architectures can become a rather complex and error prone issue. As it is good practice to keep application concerns separated from infrastructural ones, component based applications typically rely on communication middleware to cope with matters of distribution and heterogeneity. Unfortunately, generic middleware tends to be monolithic, heavyweight software, which is unacceptable in resource constrained embedded systems. Communication middleware for distributed embedded systems has to be custom tailored to the application’s interaction needs and therefore shall be as lightweight as possible. By applying the component paradigm to the communication middleware, a practical methodology can be defined, that allows the middleware’s automatic generation from the application’s architectural models and structural designs of explicit component connectors with a well defined set of prefabricated basic building blocks—so called communication primitives. This paper contributes by specifying the most common structural designs for explicit connectors within the automotive domain and thereby, in addition identifies a set of classes of automotive communication primitives. Thus this paper provides the sound foundation for automatic, model driven middleware synthesis by specifying all necessary basic modules.

scholarly journals MODES: model-based optimization on distributed embedded systems

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.

Memory concepts for enabling adaptivity in distributed embedded systems

2014 ◽  
Vol 11 (3) ◽  
pp. 66-69
Author(s):  
Philipp Schleiss ◽  
Marc Zeller ◽  
Gereon Weiss
Keyword(s):  
Embedded Systems ◽  
Distributed Embedded Systems

A decomposition-based approach to optimization of TTP-based distributed embedded systems

2018 ◽  
Vol 91 ◽  
pp. 53-61 ◽  
Author(s):  
Ming Zhang ◽  
Nenggan Zheng ◽  
Hong Li ◽  
Zonghua Gu
Keyword(s):  
Embedded Systems ◽  
Distributed Embedded Systems
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