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A resource efficient framework to run automotive embedded software on multi-core ECUs
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-1384-5323
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0001-5297-6548
RISE SICS, Västerås, Sweden.ORCID-id: 0000-0002-3375-6766
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0001-6132-7945
2018 (svensk)Inngår i: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, s. 64-83Artikkel i tidsskrift (Fagfellevurdert) Published
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 aquad-core processor, in comparison with the common framework in the literature. 

sted, utgiver, år, opplag, sider
2018. s. 64-83
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-36448DOI: 10.1016/j.jss.2018.01.040ISI: 000428493000005Scopus ID: 2-s2.0-85041901291OAI: oai:DiVA.org:mdh-36448DiVA, id: diva2:1142127
Tilgjengelig fra: 2017-09-18 Laget: 2017-09-18 Sist oppdatert: 2019-06-26bibliografisk kontrollert
Inngår i avhandling
1. Resource Optimization in Multi-processor Real-time Systems
Åpne denne publikasjonen i ny fane eller vindu >>Resource Optimization in Multi-processor Real-time Systems
2017 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

This thesis addresses the topic of resource efficiency in multiprocessor systems in the presence of timing constraints. 

 Nowadays, almost wherever you look, you find a computing system. Most computing systems employ a multiprocessor platform. Multiprocessor systems can be found in a broad spectrum of computing systems ranging from a tiny chip hosting multiple cores to large geographically-distributed cloud data centers connected by the Internet. In multiprocessor systems, efficient use of computing resources is a substantial element when it comes to achieving a desirable performance for running software applications. 

 Most industrial applications, e.g., automotive and avionics applications, are subject to a set of real-time constraints that must be met. Such kinds of applications, along with the underlying hardware and software components running 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. Therefore, in multiprocessor real-time systems, not only resource efficiency, but also meeting all the timing requirements, is a major concern. 

 Industrie 4.0 is the current trend in automation and manufacturing when it comes to creating next generation of smart factories. Two categories of multiprocessor systems play a significant role in the realization of such a smart factory: 1) multi-core processors which are the key computing element of embedded systems, 2) cloud computing data centers as the supplier of a massive data storage and a large computational power. Both these categories are considered in the thesis, i.e., 1) the efficient use of embedded multi-core processors where multiple processors are located on the same chip, applied to execute a real-time application, and 2) the efficient use of multi-processors within a cloud computing data center. We address these two categories of multi-processor systems separately. 

 For each of them, we identify the key challenges to achieve a resource-efficient design of the system. We then formulate the problem and propose optimization solutions to optimize the efficiency of the system, while satisfying all timing constraints. Introducing a resource efficient solution for those two categories of multi-processor systems facilitates deployment of Industrie 4.0 in smart manufacturing factories where multi-core embedded processors and cloud computing data centers are two central cornerstones.

sted, utgiver, år, opplag, sider
Västerås: Mälardalen University, 2017
Serie
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 263
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-35387 (URN)978-91-7485-336-0 (ISBN)
Presentation
2017-10-05, Paros, Mälardalens högskola, Västerås, 13:30 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2017-09-14 Laget: 2017-05-24 Sist oppdatert: 2018-01-13bibliografisk kontrollert
2. Optimizing Timing-Critical Cloud Resources in a Smart Factory
Åpne denne publikasjonen i ny fane eller vindu >>Optimizing Timing-Critical Cloud Resources in a Smart Factory
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Västerås: Mälardalen University, 2018
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 255
Emneord
Cloud Computing; Fog Computing; Edge Computing; Real-Time Systems; Resource Allocation
HSV kategori
Forskningsprogram
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-38659 (URN)978-91-7485-376-6 (ISBN)
Disputas
2018-03-08, Gamma, Mälardalens högskola, Västerås, 13:30 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-02-13 Laget: 2018-02-12 Sist oppdatert: 2018-06-12bibliografisk kontrollert

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