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Optimizing Timing-Critical Cloud Resources in a Smart Factory
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. hamid.faragardi@uibk.ac.at.ORCID iD: 0000-0002-1384-5323
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
Keywords [en]
Cloud Computing; Fog Computing; Edge Computing; Real-Time Systems; Resource Allocation
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-38659ISBN: 978-91-7485-376-6 (print)OAI: oai:DiVA.org:mdh-38659DiVA, id: diva2:1182307
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: 2018-06-12Bibliographically approved
List of papers
1. A Time-Predictable Fog-Integrated Cloud Framework: One Step Forward in the Deployment of a Smart Factory
Open this publication in new window or tab >>A Time-Predictable Fog-Integrated Cloud Framework: One Step Forward in the Deployment of a Smart Factory
Show others...
2018 (English)In: CSI International Symposium on Real-Time and Embedded Systems and Technologies REST'18, 2018, p. 54-62Conference paper, Published paper (Refereed)
Abstract [en]

This paper highlights cloud computing as one of the principal building blocks of a smart factory, providing a huge data storage space and a highly scalable computational capacity. The cloud computing system used in a smart factory should be time-predictable to be able to satisfy hard real-time requirements of various applications existing in manufacturing systems. Interleaving an intermediate computing layer-called fog-between the factory and the cloud data center is a promising solution to deal with latency requirements of hard real-time applications. In this paper, a time-predictable cloud framework is proposed which is able to satisfy end-to-end latency requirements in a smart factory. To propose such an industrial cloud framework, we not only use existing real-time technologies such as Industrial Ethernet and the Real-time XEN hypervisor, but we also discuss unaddressed challenges. Among the unaddressed challenges, the partitioning of a given workload between the fog and the cloud is targeted. Addressing the partitioning problem not only provides a resource provisioning mechanism, but it also gives us a prominent design decision specifying how much computing resource is required to develop the fog platform, and how large should the minimum communication bandwidth be between the fog and the cloud data center.

National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-38638 (URN)10.1109/RTEST.2018.8397079 (DOI)9781538614754 (ISBN)
Conference
CSI International Symposium on Real-Time and Embedded Systems and Technologies REST'18, 09 May 2018, Tehran, Iran
Projects
PREMISE - Predictable Multicore Systems
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2018-08-16Bibliographically approved
2. An Energy-Aware Time-Predictable Cloud Data Center
Open this publication in new window or tab >>An Energy-Aware Time-Predictable Cloud Data Center
(English)In: Software, practice & experience, ISSN 0038-0644, E-ISSN 1097-024XArticle in journal (Refereed) Submitted
Place, publisher, year, edition, pages
Sweden:
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-38637 (URN)
Projects
PREMISE - Predictable Multicore Systems
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2018-02-12Bibliographically approved
3. A Cost Efficient Design of a Multi-Sink Multi-ControllerWSN in a Smart Factory
Open this publication in new window or tab >>A Cost Efficient Design of a Multi-Sink Multi-ControllerWSN in a Smart Factory
2018 (English)In: Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017, 2018, p. 594-602Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things (IoT), one of the key elementsof a smart factory, is dubbed as Industrial IoT (IIoT). Softwaredefined networking is a technique that benefits network managementin IIoT applications by providing network reconfigurability.In this way, controllers are integrated within the networkto advertise routing rules dynamically based on network andlink changes. We consider controllers within Wireless SensorNetworks (WSNs) for IIoT applications in such a way to providereliability and timeliness. Network reliability is addressed for thecase of node failure by considering multiple sinks and multiplecontrollers. Real-time requirements are implicitly applied bylimiting the number of hops (maximum path-length) betweensensors and sinks/controllers, and by confining the maximumworkload on each sink/controller. Deployment planning of sinksshould ensure that when a sink or controller fails, the networkis still connected. In this paper, we target the challenge ofplacement of multiple sinks and controllers, while ensuring thateach sensor node is covered by multiple sinks (k sinks) andmultiple controllers (k' controllers). We evaluate the proposedalgorithm using the benchmark GRASP-MSP through extensiveexperiments, and show that our approach outperforms thebenchmark by lowering the total deployment cost by up to24%. The reduction of the total deployment cost is fulfilled notonly as the result of decreasing the number of required sinksand controllers but also selecting cost-effective sinks/controllersamong all candidate sinks/controllers.

Keywords
IoT
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-36445 (URN)10.1109/HPCC-SmartCity-DSS.2017.77 (DOI)000450718900077 ()2-s2.0-85047446022 (Scopus ID)9781538625880 (ISBN)
Conference
19th IEEE Intl Conference on High Performance Computing and Communications, 15th IEEE Intl Conference on Smart City, and 3rd IEEE Intl Conference on Data Science and Systems, HPCC/SmartCity/DSS 2017; Bangkok; Thailand; 18 December 2017 through 20 December 2017
Available from: 2017-09-18 Created: 2017-09-18 Last updated: 2018-12-06Bibliographically approved
4. A resource efficient framework to run automotive embedded software on multi-core ECUs
Open this publication in new window or tab >>A resource efficient framework to run automotive embedded software on multi-core ECUs
2018 (Swedish)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, p. 64-83Article in journal (Other academic) 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. 

National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-36448 (URN)10.1016/j.jss.2018.01.040 (DOI)000428493000005 ()2-s2.0-85041901291 (Scopus ID)
Available from: 2017-09-18 Created: 2017-09-18 Last updated: 2018-07-25Bibliographically approved
5. EAICA: An energy-aware resource provisioning algorithm for Real-Time Cloud services
Open this publication in new window or tab >>EAICA: An energy-aware resource provisioning algorithm for Real-Time Cloud services
2016 (English)In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2016Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing is receiving an increasing attention when it comes to providing a wide range of cost-effective services. In this context, energy consumption of communication and computing resources contribute to a major portion of the cost of services. On the other hand, growing energy consumption not only results in a higher operational cost, but it also causes negative environmental impacts. A large number of cloud applications in, e.g., telecommunication, multimedia, and video gaming, have real-time requirements. A cloud computing system hosting such applications, that requires a strict timing guarantee for its provided services, is denoted a Real-Time Cloud (RTC). Minimizing energy consumption in a RTC is a complicated task as common methods that are used for decreasing energy consumption can potentially lead to timing violations. In this paper, we present an online energy-aware resource provisioning framework to reduce the deadline miss ratio for real-time cloud services. The proposed provisioning framework not only considers the energy consumption of servers but it also takes the energy consumption of the communication network into account, to provide a holistic solution. An extensive range of simulation results, based on real data, show a noticeable improvement regarding energy consumption while keeping the number of timing violations less than 1% in average.

Keywords
communication-aware allocation, energy-aware scheduling, quality of service, real-time cloud services, resource provisioning, Cloud computing, Cost effectiveness, Costs, Distributed database systems, Energy utilization, Environmental impact, Factory automation, Power management, Telecommunication services, Web services, Cloud services, Communication-aware, Computing resource, Deadline miss ratio, Provisioning framework, Real time requirement, Distributed computer systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-34045 (URN)10.1109/ETFA.2016.7733574 (DOI)000389524200081 ()2-s2.0-84996587275 (Scopus ID)978-1-5090-1314-2 (ISBN)
Conference
21st IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2016, 6 September 2016 through 9 September 2016
Available from: 2016-12-08 Created: 2016-12-08 Last updated: 2018-02-12Bibliographically approved

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Citation style
  • apa
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Output format
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