Supply Chain Management (SCM) plays a vital role in business operations, necessitating careful designing and planning. Emerging technologies, such as the Internet of Things (IoT), facilitate real-time, event-driven monitoring of supply chain performance. However, integrating IoT into SCM presents significant challenges for system architects, particularly in validating requirements due to the diversity of IoT technologies and communication frameworks. Model-Based Systems Engineering (MBSE) offers a structured approach to system design by providing models and views, along with tools that automate essential processes. In this paper, we propose an MBSE-based approach for requirements validation in event-driven supply chain management. Additionally, we introduce tool support to enhance usability. An architectural overview demonstrates how system designers can customize the tool to meet their specific requirements. Our approach is designed for system architects, supporting iterative modeling until the requirements are validated as illustrated through a sample case study.

The convergence of Information Technology (IT) and Operational Technology (OT) in Industry 4.0 poses fresh challenges, demanding innovative strategies to ensure the safe execution of production processes. With the increasing significance of production system integrity, any security breaches can lead to severe consequences like production downtime, equipment damage, or human harm. Our prior research on Austrian industrial automation stakeholders highlighted the necessity for a cost-effective, all-encompassing approach to the integrated safety and security. We introduced an extensive ontology for safety, security, and operational requirements in IT/OT convergence. This paper presents an approach of Model-Based Systems Engineering (MBSE) for the integrated safety and security by design of industrial systems. We employ the Systems Modeling Language (SysML) 2.0 for precise modeling. We define metadata information that are used as tags for SysML 2.0 model instances. Afterwards, we create a graph-based model of the system. These graphs are used to validate safety and security standards and requirements. Finally, we automatically generate artifacts, such as code or documentation, which adhere to the standards. Our approach is extensible and supports reusability already after covering two standards. Having provided support for the standards IEC 62443-3-3 and IEC 61508, we reuse our approach to validate the standard ISO 13850:2015.

The domain of Industrial Internet of Things (IIoT) contains a vast variety of standards, protocols, tool suites, coding languages, etc. The Reference Architecture Model Industry (RAMI) 4.0 provides an abstraction of the different views involved in designing IIoT systems. However, the life-cycle model of the reference architecture suggests a progression from the development into production. This progression does not conform well with the best practices of software engineering, e.g., DevOps, where runtime data is given back to the design-time. In this paper, we suggest an agile life-cycle management of the IIoT systems, and propose an approach of Model-Based Systems Engineering (MBSE) based on the System Modeling Language (SysML) 2.0. We define metadata in the domain of event-driven IIoT systems. Architects can create tagged model instances, which are automatically validated against system requirements. The validated models are passed to an artifact generator to create code, test cases, or documentation. Our approach assists in the development and commissioning of system assets. We feed the runtime data to design-time to improve the quality of service.

The Industrial Internet of Things (IIoT) involves various standards, protocols, and tools, requiring extensive expertise for system design. The traditional automation pyramid limits scalability due to tightly-coupled components. Integrating IIoT data in the cloud through event-driven communication, like MQTT brokers, provides loose coupling. This integration also aids in resource monitoring and planning, enhancing IIoT application performance. Despite many IIoT architecture studies, there is a lack of empirical comparisons of MQTT broker deployment strategies. This paper examines an edge-cloud aggregation scenario, where IIoT device requests are aggregated at the edge and fog services before being transmitted to the cloud. We study four MQTT deployment architectures and compare the results empirically. We use an Arduino Opta as an IIoT device. Also, we use software-based load generation to support replicability of our experiment and reproducibility of our results. Findings indicate that a central broker on a dedicated virtual machine gives 13.8% improvements of the mean response time compared to the shared deployment of MQTT broker and device gateways. Our results offer insights into effective deployment strategies.

The Industrial Internet of Things (IIoT) is characterized by a multitude of standards, protocols, and tools. Moreover, the convergence of Information Technology (IT) and Operational Technology (OT) brings new architectural styles, e.g., event-driven communication, that are easier to scale and integrate IIoT devices. Architects need extensive expertise to manage the complex task of designing systems that encompass hardware, software, information, communication, and users. Proven approaches such as Model-Based Systems Engineering (MBSE) can simplify the design of IIoT systems by offering abstractions through system models and views. In this paper, we introduce an MBSE approach grounded in Systems Modeling Language (SysML) 2.0. Our method involves defining metadata tailored to event-driven IIoT systems. System designers can generate tagged model instances that undergo automatic validation against system requirements, such as asynchronous messaging, authentication, and health checks. Following validation, these models are utilized by an artifact generator to produce code, test cases, or documentation. Our approach is designed for reusability, and we provide tool support to streamline the implementation of requirements checking for emerging standards and guidelines. This enhances the flexibility and efficiency of IIoT system design, ensuring compliance with diverse and evolving industry requirements.

The paper addresses the challenge of efficiently and reliably transforming system models into semantically enriched runtime models by integrating ontologies to preserve semantic integrity. The approach leverages domain specialists to annotate system models with semantic tags, bridging expert perspectives and creating a unified, comprehensive representation of the system. Manually created runtime models tend to be inconsistent with the original system model. Moreover, the process is time-consuming and does not scale well for larger models. Therefore, a tool-assisted workflow is proposed that offers support at every step, from annotating the system model to generating the semantically enriched runtime model. This approach allows using definitions from semantic web ontologies in system models as tags. These semantic annotations are then used in the generation of the system's runtime model. The workflow is showcased by applying it to a system model of a Packed Bed Thermal Energy Storage (PBTES).

Dynamic reconfiguration is commonly used in service-and cloud-based applications. In combination with autoscalers, dynamic routers can adapt the system to the resource demands, e.g., in an e-commerce application offering discounts for services in a specific location. Without such measures, the quality-of-service measures are affected negatively, and a system overload can lead to an application being non-responsive. However, the cost of cloud resource usage must be considered when performing these reconfiguration steps to avoid adding high additional costs. This paper proposes a cost-aware multifaceted reconfiguration of dynamic routing applications. We study the depletion and rescheduling of idle components and use an infrastructure-as-code module to apply changes to the infrastructure. Moreover, when system components are in a steady state, our approach dynamically self-adapts between more central or distributed routing to optimize reliability and performance. This adaptation is calculated based on a system-wide optimization analysis. When components are overloaded, we perform a per-component optimization to autoscale components multidimensionally. Our extensive systematic evaluation shows significant improvements in quality trade-off adaptations and system overload prevention. We provide prototypical tool support to demonstrate our concepts with illustrative sample cases.

Many studies have been performed on integrating the Internet of Things (IoT) with cloud services. As these systems become widely used, quality metrics are of concern. For example, users might specify access control to restrict their sensitive data being processed in the cloud. Routers, e.g., API gateways, message brokers, or sidecars, can provide this access control by blocking or routing device data to a specific cloud service. However, a static routing application might not suit the dynamic behavior of IoT applications well. For example, in a centralized schema, where all device data is routed to a component for control checking, performance can be an issue. On the other hand, distributed routing can harm the reliability of a system, as device data might be lost due to an unresponsive service. We present the Smart and Adaptive Routing (SAR) architecture that creates an optimal reconfiguration solution using a deep neural network based on the quality metrics of an IoT application. To design our architecture, we give a background of the published studies and a review of the gray literature, e.g., practitioner blogs, to categorize the knowledge in the domain of IoT-cloud traffic management. We systematically evaluate our approach in an extensive evaluation of 4500 cases and compare SAR with an empirical data set of 1200 hours. The results show that our approach significantly improves quality-of-service measures by adapting the IoT-cloud system at runtime.

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.

Many dynamic routing architectural patterns are available, including distributed routing, e.g., using the sidecar pattern, or centralized routing, e.g., using event stores or service buses. Different Quality-of-Service (QoS) factors influence routing schemas and technology selection, such as performance, reliability, scalability, and control properties offered by the patterns. An analytical model can formalize the QoS factors and facilitate the architectural decision-making when changing the routing scheme, i.e., to more distributed or centralized routing. So far, the impact of these architectural patterns on performability, i.e., the overall performance of a system with impeded reliability, has not been extensively studied. This is important because deciding to increase performance, e.g., by parallel processing of requests, may lead to decreased reliability because of the added points of a crash. We propose an analytical performability model during component crashes. For the empirical validation of our proposed model, we ran an extensive experiment of 2412 hours of runtime on a private cloud infrastructure and Google Cloud Platform. The low prediction error of 1.75 % indicates the high accuracy of our performability model. These results provide important insights when making architectural decisions regarding service- and cloud-based dynamic routing.

Dynamic reconfiguration is commonly used to accommodate the dynamic behavior of today’s applications. As cloud-based systems become increasingly complex, it is hard and cost-ineffective to manage them manually. Dynamic routers, such as API Gateways or Message Brokers, in combination with auto-scalers can adapt the system to the resource demands, e.g., when a sudden load spike for a specific part of the system is observed. Without taking costs of cloud resources into account, this reconfiguration can lead to significant increase of charges. We propose a self-adaptive and cost-aware dynamic routing architecture called Adaptive Dynamic Routers. The novel architecture performs a multi-criteria optimization analysis to automatically reconfigure the routers and the services of a cloud-based system considering the costs of reconfiguration. This multidimensional auto-scaling of resources takes incoming load as an input, and uses queuing theory to find an optimal reconfiguration solution. We systematically evaluated our architecture with an extensive number of evaluation cases (9600). On average over cases where an overload is predicted, our approach reduces the overload rate by 46.7% and 61.8% for routers and services, respectively.

Container scheduling is a fundamental part of today’s service and cloud-based applications. Schedulers operate at different levels depending on how much control the system developers have. On the one hand, container orchestration managers such as Google Kubernetes manage the scheduling of containers to different nodes. On the other hand, serverless managers, such as Google Autopilot, take care of the underlying infrastructure automatically, and developers do not need to manage the nodes. However, when it comes to container depletion, i.e., removing the assigned cloud resources to an idle container, current scheduling technologies have limitations. In this paper, we propose our approach to managing cloud resource usage when containers are idle efficiently. For this purpose, we deplete idle containers statefully, i.e., propose a novel manager that monitors idle containers, saves their state, and efficiently depletes them. This manager reconstructs a depleted container using the saved state when reconstruction is needed. In our approach, we suggest an Infrastructure as Code component to automate the creation of new nodes if a depleted container cannot be scheduled on the same node, e.g., because of being overloaded. We provide an analytical model for the stateful depletion of containers and their rescheduling and empirically evaluate the accuracy of our model. For this purpose, we ran an experiment on a private cloud infrastructure and Google Cloud Platform. Our model has a low error rate of 4.28% averaged over public and private clouds.

Context:Various patterns of dynamic routing architectures are used in service- and cloud-based environments, including sidecar-based routing, routing through a central entity such as an event store, or architectures with multiple dynamic routers.Objective:Choosing the wrong architecture may severely impact the reliability or performance of a software system. This article’s objective is to provide models and empirical evidence to precisely estimate the reliability and performance impacts.Method:We propose an analytical model of request loss for reliability modeling. We studied the accuracy of this model’s predictions empirically and calculated the error rate in 200 experiment runs, during which we measured the round-trip time performance and created a performance model based on multiple regression analysis. Finally, we systematically analyzed the reliability and performance impacts and trade-offs.Results and Conclusions:The comparison of the empirical data to the reliability model’s predictions shows a low enough and converging error rate for using the model during system architecting. The predictions of the performance model show that distributed approaches for dynamic data routing have a better performance compared to centralized solutions. Our results provide important new insights on dynamic routing architecture decisions to precisely estimate the trade-off between system reliability and performance.

Many different dynamic routing architectures are available, including sidecar-based routing, routing through a central entity such as an event store or gateway, or architectures with multiple routers. These architectures are currently based on vastly different implementation concepts, such as API Gateways, Message Brokers, or Service Proxies. We propose a new approach that abstracts all these architecture patterns using one Adaptive Dynamic Routers architecture. We hypothesize that a dynamic self-adaptation of the routing architecture is beneficial over any fixed architecture selections for reliability and performance trade-offs. That is, if encountered with traffic and load changes, our approach dynamically self-adapts between more central or distributed routing to optimize system reliability and performance. We evaluate our approach by analyzing our previously-measured data during an experiment of 1200 hours of runtime. Our extensive systematic evaluation with 1089 cases confirms that our hypothesis holds and our approach is beneficial in terms of reliability and performance. Moreover, we empirically validate our results on Google Cloud Platform infrastructure.

Mature verification and monitoring approaches, such as complex event processing and model checking, can be applied for checking compliance specifications at design time and runtime. Little is known about the understandability of the different formal and technical languages associated with these approaches. This uncertainty regarding understandability might be a major obstacle for the broad practical adoption of those techniques. This article reports a controlled experiment with 215 participants on the understandability of modeling compliance specifications in representative modeling languages, namely linear temporal logic (LTL), the complex event processing-based event processing language (EPL) and property specification patterns (PSP). The formalizations in PSP were overall more correct. That is, the pattern-based approach provides a higher level of understandability than EPL and LTL. More advanced users, however, seemingly are able to cope equally well with PSP and EPL in modeling compliance specifications.

Due to technology advances and complexity of designs, thermal issue is a bottleneck in electronics designs. Various dynamic thermal management techniques have been proposed to address this issue. To effectively apply thermal management techniques, providing an accurate thermal map of chips is highly required. For this goal, a network of temperature sensors ought to be provided. There are various implementations for temperature sensors and network of sensors on Field Programmable Gate Arrays (FPGAs). This work defines and formulates four metrics and criteria, in terms of area, thermal, and power overheads and thermal map accuracy for exploring and evaluating efficiency of different implementations of Ring Oscillator-based Temperature Sensor (ROTS) networks on FPGAs and reports the comparison results for 12 networks with various sensor configurations. According to our metrics and experiments, the sensor that it is composed of NOT gates with open latches and RNS ring counter has lower thermal and power overheads compared to other configurations. Moreover, in this work, a new ROTS is presented that occupies 25% less resources than the most compact temperature sensor. Also, it provides 1.72 times higher sensitivity than the best sensitive ROTS design.