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A Resource Efficient Framework to Run Automotive Embedded Software on Multi-core ECUs
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1384-5323
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-5297-6548
RISE SICS, Västerås, Sweden.ORCID iD: 0000-0002-3375-6766
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-6132-7945
(English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228Article in journal (Refereed) Epub ahead of print
Abstract [en]

The increasing functionality and complexity of automotive applications requires not only the use of more powerful hardware, e.g., multi-core processors, but also efficient methods and tools to support design decisions. Component-based software engineering proved to be a promising solution for managing software complexity and allowing for reuse. However, there are several challenges inherent in the intersection of resource efficiency and predictability of multi-core processors when it comes to running component-based embedded software. In this paper, we present a software design framework addressing these challenges. The framework includes both mapping of software components onto executable tasks, and the partitioning of the generated task set onto the cores of a multi-core processor. This paper aims at enhancing resource efficiency by optimizing the software design with respect to 1) the inter-software components communication cost, 2) the cost of synchronization among dependent transactions of software components, and 3) the interaction of software components with the basic software services. An engine management system, one of the most complex automotive sub-systems, is considered as a use case, and the experimental results show a reduction of up to 11.2% total CPU usage on a quad-core processor, in comparison with the common framework in the literature.

Keyword [en]
Real-time scheduling, AUTOSAR, mapping, task allocation, multi-core scheduling, Ant colony optimization
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-38635DOI: 10.1016/j.jss.2018.01.040OAI: oai:DiVA.org:mdh-38635DiVA: diva2:1182244
Projects
AUTOSAR for Multi-Core in Automotive and Automation Industries
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2018-02-12Bibliographically approved
In thesis
1. Optimizing Timing-Critical Cloud Resources in a Smart Factory
Open this publication in new window or tab >>Optimizing Timing-Critical Cloud Resources in a Smart Factory
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis addresses the topic of resource efficiency in the context of timing critical components that are used in the realization of a Smart Factory.The concept of the smart factory is a recent paradigm to build future production systems in a way that is both smarter and more flexible. When it comes to realization of a smart factory, three principal elements play a significant role, namely Embedded Systems, Internet of Things (IoT) and Cloud Computing. In a smart factory, efficient use of computing and communication resources is a prerequisite not only to obtain a desirable performance for running industrial applications, but also to minimize the deployment cost of the system in terms of the size and number of resources that are required to run industrial applications with an acceptable level of performance. Most industrial applications that are involved in smart factories, e.g., automation and manufacturing applications, are subject to a set of strict timing constraints that must be met for the applications to operate properly. Such applications, including underlying hardware and software components that are used to run the application, constitute a real-time system. In real-time systems, the first and major concern of the system designer is to provide a solution where all timing constraints are met. To do so we need a time-predictable IoT/Cloud Computing framework to deal with the real-time constraints that are inherent in industrial applications running in a smart factory. Afterwards, with respect to the time predictable framework, the number of required computing and communication resources can and should be optimized such that the deployed system is cost efficient. In this thesis, to investigate and present solutions that provide and improve the resource efficiency of computing and communication resources in a smart factory, we conduct research following three themes: (i) multi-core embedded processors, which are the key element in terms of computing components embedded in the machinery of a smart factory, (ii) cloud computing data centers, as the supplier of a massive data storage and a large computational power, and(iii) IoT, for providing the interconnection of computing components embedded in the objects of a smart factory. Each of these themes are targeted separately to optimize resource efficiency. For each theme, we identify key challenges when it comes to achieving a resource-efficient design of the system. We then formulate the problem and propose solutions to optimize the resource efficiency of the system, while satisfying all timing constraints reflected in the model. We then propose a comprehensive resource allocation mechanism to optimize the resource efficiency in the whole system while considering the characteristics of each of these research themes. The experimental results indicate a clear improvement when it comes to timing-critical IoT / Cloud Computing resources in a smart factory. At the level of multi-core embedded devices, the total CPU usage of a quad-core processor is shown to be improved by 11.2%. At the level of Cloud Computing, the number of cloud servers that are required to execute a given set of real-time applications is shown to be reduced by 25.5%. In terms of network components that are used to collect sensor data, our proposed approach reduces the total deployment cost of thesystem by 24%. In summary these results all contribute towards the realization of a future smart factory.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2018
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 255
Keyword
Cloud Computing; Fog Computing; Edge Computing; Real-Time Systems; Resource Allocation
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-38659 (URN)978-91-7485-376-6 (ISBN)
Public defence
2018-03-08, Gamma, Mälardalens högskola, Västerås, 13:30 (English)
Opponent
Supervisors
Available from: 2018-02-13 Created: 2018-02-12 Last updated: 2018-02-13Bibliographically approved

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Faragardi, Hamid RezaLisper, BjörnSandström, Kristian

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