| 1. |
- Becker, Matthias, 1986-, et al.
(författare)
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A Many-Core based Execution Framework for IEC 61131-3
- 2015
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Ingår i: IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society. ; , s. 4525-4530
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Konferensbidrag (refereegranskat)abstract
- Programmable logic controllers are widely used for the control of automationsystems. The standard IEC 61131-3 defines the execution model as well as theprogramming languages for such systems. Nowadays, actuators and sensorsconnect to the programmable logic controller via automation buses. While suchbuses, as well as the sensors and actuators, become more and more powerful, ashift away from the current distributed operation of automation systems, closeto the field level, becomes possible. Instead, execution of complex controlfunctions can be relocated to more powerful hardware, and technologies. Thispaper presents an execution framework for IEC 61131-3, based on a many-coreprocessors. The presented execution model exploits the characteristics of theIEC 61131-3 applications as well as the characteristics of the many-core processor,yielding a predictable execution. We present the platform architectureand an algorithm to allocate a number of IEC 61131-3 conform applications.Experimental as well as simulation based evaluation is provided.
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| 2. |
- Becker, Matthias, 1986-
(författare)
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Efficient Resource Management for Many-Core based Industrial Real-Time Systems
- 2015
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The increased complexity of today’s industrial embedded systems stands inneed for more computational power while most systems must adhere to a restrictedenergy consumption, either to prolong the battery lifetime or to reduceoperational costs. The many-core processor is therefore a natural fit. Due tothe simple architecture of the compute cores, and therefore their good analyzability,such processors are additionally well suited for real-time applications.In our research, we focus on two particular problems which need to be addressedin order to pave the way into the many-core era. The first area is powerand thermal aware execution frameworks, where we present different energyaware extensions to well known load balancing algorithms, allowing them todynamically scale the number of active cores depending on their workload.In contrast, an additional framework is presented which balances workloadsto minimize temperature gradients on the die. The second line of works focuseson industrial standards in the face of massively parallel platforms, wherewe address the automotive and automation domain. We present an executionframework for IEC 61131-3 applications, allowing the consolidation of severalIEC 61131-3 applications on the same platform. Additionally, we discussseveral architectural options for the AUTOSAR software architecture on suchmassively parallel platforms.
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| 3. |
- Begum, Shahina, et al.
(författare)
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Towards a Compositional Service Architecture for Real-Time Cloud Robotics
- 2016
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Ingår i: ACM SIGBED Review. - : Association for Computing Machinery (ACM). - 1551-3688. ; , s. 63-64
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Tidskriftsartikel (refereegranskat)abstract
- In this paper we present our ongoing work towards a compositional service architecture that integrates cloud technology for computational capacity targeting real-time robotics applications. In particular we take a look at the challenges inherent within the data center where the services are executing. We outline characteristics of the services used in the real-time cloud robotics application, along with the service management and corresponding task model used to execute services. We identify several key central challenges that must be addressed towards integrating cloud technology in real-time robotics.
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| 4. |
- Faragardi, Hamid Reza, et al.
(författare)
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A resource efficient framework to run automotive embedded software on multi-core ECUs
- 2018
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Ingår i: Journal of Systems and Software. - : Elsevier BV. - 0164-1212 .- 1873-1228. ; 139, s. 64-83
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Faragardi, Hamid Reza, 1987-
(författare)
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Optimizing Timing-Critical Cloud Resources in a Smart Factory
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 6. |
- Leon, Miguel
(författare)
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IMPROVING DIFFERENTIAL EVOLUTION WITH ADAPTIVE AND LOCAL SEARCH METHODS
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Differential Evolution (DE) is a population-based algorithm that belongs to the Evolutionary algorithm family. During recent years, DE has become a popular algorithm in optimization due to its strength solving different types of optimization problems and due to its easy usage and implementation.However, how to choose proper mutation strategy and control parameters for DE presents a major difficulty in many real applications. Since both mutation strategy and DE control parameters are highly problem dependent, they have to be adapted to suite different search spaces and different problems. Failure in proper assignment for them will cause slow convergence in search or stagnation with a local optimum.Many researches have been conducted to tackle the above issues. The major efforts have been made in the following three directions. First, some works have been proposed that adapt the selection between various mutation strategies. But the choice of strategies in these methods has not considered the difference of quality of individuals in the population, which means that all individuals will acquire the same probability to select a mutation strategy from the candidates. This does not seem a very desired practice since solutions of large difference would require different mutation operators to reach improvement. Second, many works have been focusing on the adaptation of the control parameters of DE (mutation factor (F) and crossover rate (CR)). They mainly rely on previous successful F and CR values to update the probability functions that are used to generate new F and CR values. By doing this, they ignore the stochastic nature of the operators in DE such that weak F and CR values can also get success in producing better trial solutions. The use of such imprecise experiences of success would prevent the DE parameters from being adapted towards the most effective values in coming generations. Third, various local search methods have been incorporated into DE to enhance exploitation in promising regions so as to speed up the convergence to optima. It is important to properly adjust the characteristics of the local search in DE to achieve well balanced exploratory/exploitative behavior to solve complex optimization problems.This thesis aims to further improve the performance of DE by new adaptation and local search methods. The main results can be summarized in the following three aspects:1) Proposal of a new rank-based mutation adaptation method, which takes into account the quality of solutions in the population when adapting the selection probabilities of mutation strategies. This makes possible to treat solutions with distinct ranks (in quality) differently by using different selection probabilities for mutation operators.2) Development of improved parameter adaptation methods for DE, which emphasizes more reliable and fair evaluation of candidates (F and CR assignments) during the search process. It is suggested that greedy search being used as a fast and cheap technique to look for better parameter assignment for F and CR respectively in the neighborhood of a current candidate. Further, a joint parameter adaptation method is proposed that enables continuous update of the selection probabilities for F and CR pairs based on feedback acquired during the search.3) Proposal of new methods for better incorporation of local search into a DE algorithm. The Eager Random Search method is investigated as local search inside DE, which exhibits different exploratory-exploitative characteristics by using different probability density functions. More importantly, we propose a novel memetic framework in which Alopex local search (ALS) is performed in collaboration with a DE algorithm. The framework favors seamless connection between exploration and exploitation in the sense that the behavior of exploitation by ALS can be controlled by the status of global exploration by DE.The proposed methods and algorithms have been tested in a number of benchmark problems, obtaining competitive results compared with the state-of-the-art algorithms. Additionally, the Greedy Adaptive DE (GADE) algorithm (developed based on greedy search for DE parameters) has been tested in a real industrial problem, i.e., finding best component parameters to optimize the performance of harmonic filters for power transmission. GADE is shown to produce better harmonic filter systems with lower harmonic distortion than the standard DE.
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| 7. |
- Liu, Meng, et al.
(författare)
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On providing real-time guarantees in cloud-based platforms
- 2016
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Ingår i: IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS. - 9781509023394
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Konferensbidrag (refereegranskat)abstract
- Cloud technologies are gaining more and more attentions in recent years. Cloud-based service brings benefits in cost, energy efficiency, sharing of resources, increased flexibility, adaptability and evolvability. However, there are a number of associated challenges that need to be properly addressed before applying the cloud technique generally in industries. Providing efficient and predictable computation and communication is one of the important challenges, since many industrial systems (e.g. a control system) have specific timing requirements. Our current work thus focuses on guaranteeing the predictability of a cloud-based service. Virtualization, as one of the key technologies in Cloud Computing, is used to abstract details of resources away from end-services which simplifies the resource sharing. It thus improves the resource utilization and saves budget for end-users. In this preliminary work, we have implemented a distributed system using virtualization techniques (including virtual machines and virtual switches). Additionally, we generate a number of experiments to investigate how QoS policies can help us to provide real-time communication guarantees.
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| 8. |
- Mubeen, Saad, et al.
(författare)
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Delay Mitigation in Offloaded Cloud Controllers in Industrial IoT
- 2017
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 5, s. 4418-4430
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Tidskriftsartikel (refereegranskat)abstract
- This paper investigates the interplay of cloud computing, fog computing, and Internet of Things (IoT) in control applications targeting the automation industry. In this context, a prototype is developed to explore the use of IoT devices that communicate with a cloud-based controller, i.e., the controller is offloaded to cloud or fog. Several experiments are performed to investigate the consequences of having a cloud server between the end device and the controller. The experiments are performed while considering arbitrary jitter and delays, i.e., they can be smaller than, equal to, or greater than the sampling period. This paper also applies mitigation mechanisms to deal with the delays and jitter that are caused by the networks when the controller is offloaded to the fog or cloud.
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| 9. |
- Mustafa, Jawad, et al.
(författare)
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Analyzing availability and QoS of service-oriented cloud for industrial IoT applications
- 2019
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Ingår i: 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). ; , s. 1403-1406
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Konferensbidrag (refereegranskat)abstract
- Internet of Things and cloud services are one of main enablers in fourth industrial revolution. Real-time industrial systems have high availability requirements of 99.9% to 99.999% whereas architectures built on regional cloud services and IoT do not provide similar guarantees or Service Level Agreement. These differences of QoS and SLA availability between Operational Technology and Information Technology has become a main challenge in adoption of Industrial Internet of Things (IIoT) for real-time applications.This work presents an approach to find end-to-end QoS and availability for an IIoT architecture. Device-to-cloud, cloud-to-cloud and inside-cloud experiments have been performed over eight weeks where each experiment have more then four million QoS measurements. Our availability analysis shows that a remote IoT connected to a less busy cloud region gives higher availability as compared to an IoT device inside a busy cloud region. IIoT and regional cloud services provide good QoS with 99% to 99.9% availability for 1sec soft real-time requirements. In 100ms applications, more efforts are required to achieve higher then 95% availability and design industrial SLA. IIoT applications with 10sec latency like machine learning models can get 99.9% availability with cloud. Availability loss due to communication is almost 1% for 100ms applications. These results also provide requirements and future work of industrial edge computing for IIoT on real-time cloud.
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| 10. |
- Nikolaidis, Pavlos, et al.
(författare)
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Applying Mitigation Mechanisms for Cloud-based Controllers in Industrial IoT Applications
- 2015
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Ingår i: Internet-of-Things Symposium IoT Symposium'15.
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Konferensbidrag (refereegranskat)abstract
- Cloud computing and Internet of Things (IoT) are two notable concepts that have evolved significantly over the past few years. In the automation industry, clouds are often used for monitoring vast amounts of data generated on the shop floor. Whereas, IoT is used to simplify the end devices and their connections to the rest of the system. In this paper we investigate the interplay of these two concepts and their use in the control applications in the automation industry. We develop a prototype in the industrial setup to explore the use of IoT devices that communicate with a cloud-based controller. Using the prototype, we perform a number of experiments to investigate the consequences of having a cloud server between the end device and the controller. Within this context we consider arbitrary jitter and delays, i.e., they can be smaller, equal or greater than the sampling periods. Moreover, we apply mitigation mechanisms to deal with the delays and jitter that are caused by the local and wide area networks (LAN and WAN).
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