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A hybrid cloud is an efficient solution to deal with the problem of insufficient resources of a private cloud when computing demands increase beyond its resource capacities. Cost-efficient workflow scheduling, considering security requirements and data dependency among tasks, is a prominent issue in the hybrid cloud. To address this problem, we propose a mathematical model that minimizes the monetary cost of executing a workflow and satisfies the security requirements of tasks under a deadline. The proposed model fulfills data dependency among tasks, and data transmission time is formulated with exact mathematical expressions. The derived model is a Mixed-integer linear programming problem. We evaluate the proposed model with real-world workflows over changes in the input variables of the model, such as the deadline and security requirements. This paper also presents a post-optimality analysis that investigates the stability of the assignment problem. The experimental results show that the proposed model minimizes the cost by decreasing inter-cloud communications for dependent tasks. However, the optimal solutions are affected by the limitations that are imposed by the problem constraints. 

Time-Sensitive Networking (TSN) is a set of standards with significant industrial impact potential, primarily due to its ability to integrate multiple traffic types with different requirements, offering great network flexibility. Among these traffic types, Audio-Video Bridging (AVB) stands out for its real-time guarantees and dynamic scheduling. In order to guarantee a specific set of AVB frames meet their timing requirements a Worst-Case Response Time Analysis (WCRTA) is essential. Unfortunately, current WCRTAs are often overly conservative, failing to guarantee schedulability for TSN systems operating even under low bandwidth conditions. This limits the practical usefulness of these analyses. Since TSN utilizes a multi-hop architecture, most WCRTAs analyze each link independently and then add the contributions. This compartmental analysis introduces pessimism, particularly when calculating the interference caused by other AVB frames with the same priority as the frame under analysis. In this paper, we address this issue by refining the Same-Priority Interference (SPI) calculation, leading to a significant improvement in the schedulability of WCRTAs and, consequently, the overall efficiency of TSN networks.

Time-Sensitive Networking (TSN) has become one of the most relevant communication networks in many application areas. Among several trafik classes supported by TSN networks, Audio-Video Bridging (AVB) traffk requires a Worst-Case Re­sponse Time Analysis (WCRTA) to ensure that AVB frames meet their time requirements. In this paper, we evaluate the existing WCRTAs that cover various features of TSN, including Scheduled Trafik (ST) interference and preemption. When considering the effect of the ST interference, we detect optimism problems in two of the existing WCRTAs, namely (i) the analysis based on the busy period calculation and (ii) the analysis based on the eligible interval. Therefore, we propose a new analysis including a new ST interference calculation that can extend the analysis based on the eligible interval approach. The new analysis covers the effect of the ST interference, the preemption by the ST traffic, and the multi-hop architecture. The resulting WCRTA, while safe, shows a significant improvement in terms of pessimism level compared to the existing analysis approaches relying on either the concept of busy period or the Network Calculus model. 

In this paper, we present a bandwidth reservation analysis for Audio-Video Bridging (AVB) traffic in the Time-Sensitive Networking (TSN) standards. The proposed analysis is based on the existing worst-case response-time analysis and can be used to calculate the minimum required bandwidth for guaranteeing the schedulability of messages in AVB classes. The proposed analysis allocates per-link bandwidth to AVB traffic that is sufficient to ensure its schedulability when a combination of the Credit-Based Shaper and Time-Aware Shaper mechanisms are used. We evaluate the proposed analysis using an automotive industrial use case. We evaluate the schedulability of AVB traffic by comparing the proposed analysis with the utilization-based bandwidth reservation as recommended by the TSN standards.

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.

This paper explores the integration of Time-Sensitive Networking (TSN) with 5G cellular networks to support high-bandwidth and low-latency end-to-end communication in networked embedded systems. Integrating TSN with 5G has the potential to support predictable and deterministic end-to-end communication, as well as to significantly enhance scalability, particularly in industrial automation, by providing flexibility, efficiency, and responsiveness. To ensure smooth integration while preserving TSN's Quality of Service (QoS) requirements, effective traffic translation and forwarding within the network are crucial. In this regard, this paper addresses key challenges related to traffic translation, QoS implementation, and latency in both TSN and private 5G networks on a realistic scenario. Through experiments, measurements, and evaluation, this paper thoroughly assesses latency and network capabilities in the integrated networks. Understanding these metrics is essential for devising effective integration strategies. Our findings indicate that it is possible to achieve latencies under 20 ms in an integrated TSN-5G network, given our specific configuration of a private 5G setup with a channel bandwidth of 40 MHz. We also identify an urgent need for the implementation of a proper QoS mechanism in the Open Air Interface software to enable the prioritization of high-critical data transmission.

Container-based virtualization is a promising deployment model in fog and edge computing applications, because it allows a seamless co-existence of virtualized applications in a heterogeneous environment without introducing significant overhead. Certain application domains (e.g., industrial automation, automotive, or aerospace) mandate that applications exhibit a certain degree of temporal predictability. Container-based virtualization cannot be easily used for such applications, since the technology is not designed to support real-time properties and handle temporal disturbances. This article proposes a framework consisting of a static offline and a dynamic online phase for resource allocation and adaptive re-dimensioning of real-time containers. In the offline phase, the optimal initial deployment and dimensioning of containers are decided based on ideal system models. Additionally, to adapt to dynamic variations caused by changing workloads or interferences, the online phase adapts the CPU usage and limits of real-time containers at runtime to improve the real-time behavior of the real-time containerized applications while optimizing resource usage. We implement the framework in a real Linux-based system and showthrough a series of experiments that the proposed framework is able to adjust and re-distribute computing resources between containers to improve the real-time behavior of containerized applications in the presence of temporal disturbances while optimizing resource usage.

In an Industrial Control System (ICS), link failures causing partitioning between the redundant controller pair can prevent synchronization. This disruption could potentially trigger incorrect redundancy role-switching procedures. Distinguishing link and node failures from each controller's perspective can effectively mitigate these dependability threats. We introduce an algorithm capable of detecting and differentiating link failures from node failures within a bounded time. The proposed algorithm leverages periodic messages generated by industrial protocols operational in network devices. Our preliminary evaluation indicates the feasibility of the proposed algorithm in terms of meeting the real-time requirements of the ICSs.

Industrial automation and control systems are responsible for running our most important infrastructures, providing electricity and clean water, producing medicine and food, along with many other services and products we take for granted. The safe and secure operation of these systems is therefore of great importance. Reliability is a fundamental requirement, with spatial controller redundancy being one important fault-tolerance mechanism. In current control system solutions, controller redundancy is implemented using state and heartbeat transfer communicated over dedicated physical links, based on an assumption of implicit trust. However, with the emerging network-centric control strategies there is a desire to transition into dynamic redundancy scenarios, where such dedicated links are unfeasible. In this paper, an approach for a threat-model based security analysis is presented from the perspective of the automation industry. The approach is applied to the communication links used for supporting control system redundancy within a network-centric architecture.