Isolation, autonomy, and loose coupling are critical success factors of microservice architectures, but unfortunately, systems tend to become strongly coupled over time and sometimes even exhibiting cyclic communicati...
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In the domain of Industry 4.0 Cyber-Physical Production Systems (CPPSs), Reinforcement Learning (RL) has gained momentum as an effective strategy for training intelligent agents in digital twins. Whilst the practice o...
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ISBN:
(数字)9798331520908
ISBN:
(纸本)9798331520915
In the domain of Industry 4.0 Cyber-Physical Production Systems (CPPSs), Reinforcement Learning (RL) has gained momentum as an effective strategy for training intelligent agents in digital twins. Whilst the practice of Machine Learning Operations (MLOps) has become established as a holistic approach to automating workflows in supervised and unsupervised Machine Learning (ML), the extent to which MLOps practices are applicable to RL, particularly due to major differences between ML and RL concerning model deployment and model training, are not currently well-understood. The literature on RLOps as a paradigm is scarce. We tackle this open question by conducting an exploratory, qualitative, deductive-inductive industry case study on a CPPS, performing content analysis of CPPS artefacts, such as architectural schematics and source code, and understanding their relation to 22 known Architectural Design Decisions and 86 associated decision options through classification into four distinct emergent categories. Our findings help bridge the gap between MLOps and RLOps architectures, contributing novel insights into understanding the application of MLOps to RL and providing practical insights and inspiration for further research.
Designing Cyber-Physical Systems (CPS) is a complex task involving integrating physical and digital components to achieve specific objectives. This process consolidates data from various Internet of Things (IoT) devic...
Designing Cyber-Physical Systems (CPS) is a complex task involving integrating physical and digital components to achieve specific objectives. This process consolidates data from various Internet of Things (IoT) devices and sources to generate meaningful insights and actionable outcomes. IoT-cloud data communication comprises multiple stages, e.g., data collection, processing, analysis, and visualization. Adopting a comprehensive approach that considers physical and digital aspects is essential to ensure effective data communication in CPS. As a result, architectural design choices are crucial in determining CPS functionality and runtime qualities, e.g., performance, security, and reliability. While numerous CPS architectural patterns and practices have been proposed, much of the relevant knowledge remains scattered across various sources, such as practitioner blogs and system documentation. These sources are often based on personal experiences and lack consistency. To address this gap, our study presents the outcomes of an in-depth qualitative investigation into practitioners' descriptions of the best practices and patterns in CPS architecture. We have developed a formal architectural decision model using a model-based qualitative research method. We aim to bridge the division between scientific understanding and practical use cases, enhance comprehension of practitioners' approaches to CPS, and provide decision-making support for designing CPS applications.
We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/...
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There are many challenges in maintaining the desired quality of service levels in modern microservice and cloud applications. Numerous techniques and patterns, such as API Rate Limit, Load Balancing, and Request Bundl...
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This paper presents a tool relying on data service architecture, where technical details of all VBS datasets are completely hidden behind an abstract stateless data layer. The data services allow independent developme...
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This paper delves into the importance of addressing the data clumps model smell, emphasizing the need for prioritizing them before refactoring. Qualitative and quantitative criteria for identifying data clumps are out...
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