scholarly journals Control-Quality Optimization for Distributed Embedded Systems with Adaptive Fault Tolerance

2012 ◽  
Author(s):  
Soheil Samii ◽  
Unmesh D. Bordoloi ◽  
Petru Eles ◽  
Zebo Peng ◽  
Anton Cervin
Keyword(s):  
Fault Tolerance ◽  
Embedded Systems ◽  
Distributed Embedded Systems ◽  
Quality Optimization ◽  
Control Quality

A New Intelligent Biologically-Inspired Model for Fault Tolerance in Distributed Embedded Systems

2020 ◽  
Vol 11 (3) ◽  
pp. 22-47
Author(s):  
Ridha Mehalaine ◽  
Fateh Boutekkouk
Keyword(s):  
Fault Tolerance ◽  
Embedded Systems ◽  
Point Of View ◽  
Uncertain Information ◽  
Periodic Tasks ◽  
Distributed Embedded Systems ◽  
Biologically Inspired ◽  
Dynamic Task ◽  
Multiple Processors ◽  
Dynamic Voltage

The objective of this work is to present a new heuristic for solving the problem of fault tolerance in real time distributed embedded systems. The proposed idea is to model the distributed embedded architecture inspiring from the rennin-angiotensin aldosterone (RAAS) biological system which plays a major role in the pathophysiology of the cardiovascular system, from the point of view of pressure regulation and vascular, cardiac and nephrological remodeling. The proposed heuristic deals with uncertain information on a set of periodic tasks that run on multiple processors and satisfies certain temporal and energetic constraints from which the scheduling and the distribution of these tasks on the different processors are performed. In order to respect the energy constraints, this article proposes the introduction of energy consumption at the dynamic task scheduling level by using the dynamic voltage scaling (DVS) technique. The authors have seen that the introduction of a detection/prevention mechanism against potential errors in the proposed algorithm is a must for good results.

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
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