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Clouds are large computer centers that offer remote access to computing and storage resources, making them popular for business and web applications. They are now being considered for use in safety-critical applications such as factories, but lack sufficient time predictability, which makes it challenging to use them in these time-sensitive applications. To overcome this limitation, an intermediate layer, the fog layer, is introduced to provide computational resources closer to the network edge. However, this new computing paradigm faces its own challenges in resource management, scalability, and reliability due to resource constrained nodes. Lightweight virtualization technologies like containerization can solve the performance-reliability dichotomy in fog computing and provide built-in fault tolerance mechanisms. By studying a robotic use-case, we realized the critical importance of persistent data storage for stateful applications, such as many control applications. However, container-based solutions lack fault-tolerant persistent storage. In this thesis, we identify new challenges associated with leveraging container-based architectures, particularly the importance of persistent storage for stateful applications. We investigate the design possibilities for persistent fault-tolerant storage and propose a solution adapted to container-based fog architectures and tailored for stateful applications. The solution provides scalability, auto recovery, and re-integration after failures at application and node levels. Key elements are a replicated data structure and a storage container, using a consensus protocol for distributed data consistency and fault tolerance in case of node failures. The fault tolerance and consistency of the solution are modeled and verified, and its timing requirements evaluated. We use simulation to evaluate the timing performance of our solution in larger set-ups. The results of our study show that although adding a consistency protocol introd

Cloud computing provides computing and storage resources over the Internet to provide services for different industries. However, delay-sensitive applications like smart health and city applications now require computation over large amounts of data transferred to centralized cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide new solutions by bringing resources closer to the user and provide low latency and energy efficiency compared to cloud services. It is important to find optimal placement of services and resources in the three-tier IoT to achieve improved cost and resource efficiency, higher QoS, and higher level of security and privacy. In this paper, we propose a cost-aware genetic-based (CAG) task scheduling algorithm for fog-cloud environments, which improves the cost efficiency in real-time applications with hard deadlines …

This paper analyzes the timing performance of a persistent storage designed for distributed containerbased architectures in industrial control applications. The storage ensures data availability andconsistency while accommodating faults. The analysis considers four aspects: 1. placement strategy,2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet criticaldeadlines, particularly under specific failure patterns. Moreover, this evaluation method is applicablefor assessing other container-based critical applications with timing constraints that require persistentstorage. Further comparison results reveal that, while the method may underperform current centralized solutions under fault-free conditions, it outperforms the centralized solutions in failure scenarios

This paper analyzes the timing performance of a persistent storage designed for distributed containerbased architectures in industrial control applications. The storage ensures data availability andconsistency while accommodating faults. The analysis considers four aspects: 1. placement strategy,2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet criticaldeadlines, particularly under specific failure patterns. Moreover, this evaluation method is applicablefor assessing other container-based critical applications with timing constraints that require persistentstorage. Further comparison results reveal that, while the method may underperform current centralized solutions under fault-free conditions, it outperforms the centralized solutions in failure scenarios

This paper analyzes the timing performance of a persistent storage designed for distributed containerbased architectures in industrial control applications. The storage ensures data availability andconsistency while accommodating faults. The analysis considers four aspects: 1. placement strategy,2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet criticaldeadlines, particularly under specific failure patterns. Moreover, this evaluation method is applicablefor assessing other container-based critical applications with timing constraints that require persistentstorage. Further comparison results reveal that, while the method may underperform current centralized solutions under fault-free conditions, it outperforms the centralized solutions in failure scenarios

This paper analyzes the timing performance of a persistent storage designed for distributed containerbased architectures in industrial control applications. The storage ensures data availability andconsistency while accommodating faults. The analysis considers four aspects: 1. placement strategy,2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet criticaldeadlines, particularly under specific failure patterns. Moreover, this evaluation method is applicablefor assessing other container-based critical applications with timing constraints that require persistentstorage. Further comparison results reveal that, while the method may underperform current centralized solutions under fault-free conditions, it outperforms the centralized solutions in failure scenarios