| 1. |
- Abdi, Somayeh, et al.
(author)
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Cognitive and Time Predictable Task Scheduling in Edge-cloud Federation
- 2022
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In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665499965
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Conference paper (peer-reviewed)abstract
- In this paper, we present a hierarchical model for time predictable task scheduling in edge-cloud computing architecture for industrial cyber-physical systems. Regarding the scheduling problem, we also investigate the common problem-solving approaches and discuss our preliminary plan to realize the proposed architecture. Furthermore, an Integer linear programming (ILP) model is proposed for task scheduling problem in the cloud layer. The model considers timing and security requirements of applications and the objective is to minimize the financial cost of their execution.
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| 2. |
- Abdi, Somayeh, et al.
(author)
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Cost-aware workflow offloading in edge-cloud computing using a genetic algorithm
- 2024
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In: Journal of Supercomputing. - : Springer. - 0920-8542 .- 1573-0484.
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Journal article (peer-reviewed)abstract
- The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs.
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| 3. |
- Abdi, Somayeh, et al.
(author)
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Task Offloading in Edge-cloud Computing using a Q-Learning Algorithm
- 2024
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In: International Conference on Cloud Computing and Services Science, CLOSER - Proceedings. - : Science and Technology Publications, Lda. - 9789897587016 ; , s. 159-166
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Conference paper (other academic/artistic)abstract
- Task offloading is a prominent problem in edge−cloud computing, as it aims to utilize the limited capacityof fog servers and cloud resources to satisfy the QoS requirements of tasks, such as meeting their deadlines.This paper formulates the task offloading problem as a nonlinear mathematical programming model to maximizethe number of independent IoT tasks that meet their deadlines and to minimize the deadline violationtime of tasks that cannot meet their deadlines. This paper proposes two Q-learning algorithms to solve theformulated problem. The performance of the proposed algorithms is experimentally evaluated with respect toseveral algorithms. The evaluation results demonstrate that the proposed Q-learning algorithms perform wellin meeting task deadlines and reducing the total deadline violation time.
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| 4. |
- Agerskans, Natalie, et al.
(author)
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Critical Factors for Selecting and Integrating Digital Technologies to Enable Smart Production : A Data Value Chain Perspective
- 2023
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In: IFIP Advances in Information and Communication Technology. - : Springer Science and Business Media Deutschland GmbH. - 9783031436611 ; , s. 311-325
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Conference paper (peer-reviewed)abstract
- With the development towards Industry 5.0, manufacturing companies are developing towards Smart Production, i.e., using data as a resource to interconnect the elements in the production system to learn and adapt accordingly for a more resource-efficient and sustainable production. This requires selecting and integrating digital technologies for the entire data lifecycle, also referred to as the data value chain. However, manufacturing companies are facing many challenges related to building data value chains to achieve the desired benefits of Smart Production. Therefore, the purpose of this paper is to identify and analyze the critical factors of selecting and integrating digital technologies for efficiently benefiting data value chains for Smart Production. This paper employed a qualitative-based multiple case study design involving manufacturing companies within different industries and of different sizes. The paper also analyses two Smart Production cases in detail by mapping the data flow using a technology selection and integration framework to propose solutions to the existing challenges. By analyzing the two in-depth studies and additionally two reference cases, 13 themes of critical factors for selecting and integrating digital technologies were identified.
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| 5. |
- Agerskans, Natalie
(author)
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Digital Technologies for Enabling Smart Production : Examining the Aspects of Selection and Integration
- 2023
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Licentiate thesis (other academic/artistic)abstract
- With the development towards Industry 5.0, manufacturing companies are developing towards smart production. In smart production, data is used as a resource to interconnect different elements in the production system to learn and adapt to changing production conditions. Common objectives include human-centricity, resource-efficiency, and sustainable production. To enable these desired benefits of smart production, there is a need to use digital technologies to create and manage the entire flow of data. To enable smart production, it is essential to deploy digital technologies in a way so that collected raw data is converted into useful data that can be applied by equipment or humans to generate value or reduce waste in production. This requires consideration to the data flow within the production system, i.e., the entire process of converting raw data into useful data which includes data management aspects such as the collection, analysis, and visualization of data. To enable a good data flow, there is a need to combine several digital technologies. However, many manufacturing companies are facing challenges when selecting suitable digital technologies for their specific production system. Common challenges are related to the overwhelming number of advanced digital technologies available on the market, and the complexity of production system and digital technologies. This makes it a complex task to understand what digital technologies to select and the recourses and actions needed to integrate them in the production system.Against this background, the purpose of this licentiate thesis is to examine the selection and integration of digital technologies to enable smart production within manufacturing companies. More specifically, this licentiate thesis examines the challenges and critical factors of selecting and integrating digital technologies for smart production. This was accomplished by performing a qualitative-based multiple case study involving manufacturing companies within different industries and of different sizes. The findings show that identified challenges and critical factors are related to the different phases of the data value chain: data sources and collection, data communication, data processing and storage, and data visualisation and usage. General challenges and critical factors that were related to all phases of the data value chain were also identified. Moreover, the challenges and critical factors were related to people, process, and technology aspects. This shows that there is a need for holistic perspective on the entire data value chain and different production system elements when digital technologies are selected and integrated. Furthermore, there is a need to define a structured process for the selection and integration of digital technologies, where both management and operational level are involved.
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| 6. |
- Agerskans, Natalie, et al.
(author)
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Enabling Smart Production : The Role of Data Value Chain
- 2022
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In: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031164101 ; , s. 477-485
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Conference paper (peer-reviewed)abstract
- To stay competitive, manufacturing companies are developing towards Smart Production which requires the use of digital technologies. However, there is a lack of guidance supporting manufacturing companies in selecting and integrating a combination of suitable digital technologies, which is required for Smart Production. To address this gap, the purpose of this paper is twofold: (i) to identify the main challenges of selecting and integrating digital technologies for Smart Production, and (ii) to propose a holistic concept to support manufacturing companies in mitigating identified challenges in order to select and integrate a combination of digital technologies for Smart Production. This is accomplished by using a qualitative-based multiple case study design. This paper identifies current challenges related to selection and integration of digital technologies. To overcome these challenges and achieve Smart production, the concept of data value chain was proposed, i.e., a holistic approach to systematically map and improve data flows within the production system. © 2022, IFIP International Federation for Information Processing.
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| 7. |
- Al-Dulaimy, Auday, et al.
(author)
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Fault Tolerance in Cloud Manufacturing : An Overview
- 2023
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In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST. - : Springer Science and Business Media Deutschland GmbH. - 9783031318900 ; , s. 89-101
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Conference paper (peer-reviewed)abstract
- Utilizing edge and cloud computing to empower the profitability of manufacturing is drastically increasing in modern industries. As a result of that, several challenges have raised over the years that essentially require urgent attention. Among these, coping with different faults in edge and cloud computing and recovering from permanent and temporary faults became prominent issues to be solved. In this paper, we focus on the challenges of applying fault tolerance techniques on edge and cloud computing in the context of manufacturing and we investigate the current state of the proposed approaches by categorizing them into several groups. Moreover, we identify critical gaps in the research domain as open research directions. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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| 8. |
- Al-Dulaimy, Auday, et al.
(author)
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TOLERANCER : A fault tolerance approach for cloud manufacturing environments
- 2022
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In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665499965
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Conference paper (peer-reviewed)abstract
- The paper presents an approach to solve the software and hardware related failures in edge-cloud environments, more precisely, in cloud manufacturing environments. The proposed approach, called TOLERANCER, is composed of distributed components that continuously interact in a peer to peer fashion. Such interaction aims to detect stress situations or node failures, and accordingly, TOLERANCER makes decisions to avoid or solve any potential system failures. The efficacy of the proposed approach is validated through a set of experiments, and the performance evaluation shows that it responds effectively to different faults scenarios.
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| 9. |
- Alvarez Vadillo, Ines, et al.
(author)
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Centralised Architecture for the Automatic Self-Configuration of Industrial Networks
- 2023
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In: IEEE Int. Conf. Emerging Technol. Factory Autom., ETFA. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350339918
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Conference paper (peer-reviewed)abstract
- Novel production paradigms aim at increasing the efficiency and flexibility of production systems. Nonetheless, traditional industrial infrastructures lack the mechanisms needed to support these new paradigms. One of the main limiting factors is the architecture, which follows the automation pyramid in which subsystems are divided in layers depending on their functionalities. This allowed to meet the timing and dependability requirements of the production subsystems, however at the cost of limiting the exchange of information required to provide increased flexibility to the system. For this reason, in this paper we propose a new industrial architecture with a single network infrastructure to connect all the devices that conform to the industrial systems. On top of that, we design an Automatic Network Configurator to support the automatic configuration of the system. To assess the feasibility of our design and evaluate its performance, we implement the first instance of the architecture capable of supporting changes in the traffic requirements during run-time, i.e., without stopping or disrupting the system's operation. Furthermore, we use the implemented instance to measure the time required for reconfigurations.
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| 10. |
- Alvarez Vadillo, Ines, et al.
(author)
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Implementing a First CNC for Scheduling and Configuring TSN Networks
- 2022
-
In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665499965
-
Conference paper (peer-reviewed)abstract
- Novel industrial applications are leading to important changes in industrial systems. One of the most important changes is the need for systems that are capable to adapt to changes in the environment or the system itself. Because of their nature many of these applications are distributed, and their network infrastructure is key to guarantee the correct operation of the overall system. Furthermore, in order for a distributed system to be able to adapt, its network must be flexible enough to support changes in the traffic during runtime. The Time-Sensitive Networking (TSN) Task Group has proposed a series of standards that aim at providing deterministic real-time communications over Ethernet. TSN also provides centralised online configuration and control architectures which enable the online configuration of the network. A key part in TSN's centralised architectures is the Centralised Network Configuration element (CNC). In this work we present a first implementation of a CNC capable of scheduling time-triggered traffic and deploying such configuration in the network using the Network Configuration (NETCONF) protocol. We also assess the correctness of our implementation using an industrial use case provided by Volvo Construction Equipment.
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