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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.

Fault tree analysis is a system malfunction hazard evaluation quantitative and qualitative procedure. The method is well-known and widely used, especially in the safety systems domain, where it is a mandatory integral part of the so-called "Hazard Evaluation"documentation. This paper proposes an alternative or complementary deductive fault analysis method: it uses system topology to build a hypergraph representation of the system to identify component criticality and support loss of functionality probability evaluation. Once automated, the proposed method seems promising when the system engineers explore the different architectures. They may have indication about architecture's reliability without continuous feedback from the system safety team. The system safety team must check the solution once the engineers select the final architecture. They can also use the proposed method to validate the correctness of the fault tree analysis.

The users of cloud services prioritize cost andperformance, but they increasingly demand sustainable practices.Sustainability is no longer a choice for businesses but a strategicimperative that shapes global industries. This paper presents anew system for utilizing the render farms in cloud data centers.The system aims to reduce energy consumption and costs in clouddata centers while maintaining a specific level of performance,particularly when rendering images and videos. The system canbe described as a cloud-based expert system that offers renderingas a service, while considering user preferences for performance,cost, and energy efficiency. The system reads different scenerendering parameters and accordingly chooses the most suitableGPUs that fit the user’s requirements. In other words, thesystem inputs are the scene complexity and user preferences.The output is the optimal GPU for rendering. Scene complexityis determined based on several parameters, such as the numberof frames and polygons, resulting in one scene-related value.The user preferences are also normalized to a preferences-relatedvalue. Then, these values are aggregated to determine the optimalavailable GPU to render the scene at the lowest cost, minimumpossible energy consumption, and highest performance.

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 real-time systems analysis, probabilistic models, particularly Markov chains, have proven effective for tasks with dependent executions. This paper improves upon an approach utilizing Gaussian emission distributions within a Markov task execution model that analyzes bounds on deadline miss probabilities for tasks in a reservation-based server. Our method distinctly addresses the issue of runtime complexity, prevalent in existing methods, by employing a state merging technique. This not only maintains computational efficiency but also retains the accuracy of the deadline-miss probability estimations to a significant degree. The efficacy of this approach is demonstrated through the timing behavior analysis of a Kalman filter controlling a Furuta pendulum, comparing the derived deadline miss probability bounds against various benchmarks, including real-time Linux server metrics. Our results confirm that the proposed method effectively upper-bounds the actual deadline miss probabilities, showcasing a significant improvement in computational efficiency without significantly sacrificing accuracy.

This study is based on a Moving Target Defence (MTD) algorithm designed to introduce uncertainty into the controller and another layer of uncertainty to intrusion detection. This randomness complicates the adversary's attempts to craft stealthy attacks while concurrently minimizing the impact of false-data injection attacks. Leveraging concepts from state observer design, the method establishes an optimization framework to determine the parameters of the random signals. These signals are strategically tuned to increase the detectability of stealthy attacks while reducing the deviation resulting from false data injection attempts. We propose here to use two different state observers and two associated MTD algorithms. The first one optimizes the parameters of the random signals to reduce the deviation resulting from false data injection attempts and maintain the stability of the closed-loop system with the desired level of performance. In contrast, the second one optimizes the parameters of the random signals to increase the detectability of stealthy attacks. Dividing the optimization problem into two separate optimization processes simplifies the search process and makes it possible to have higher values of the detection cost function. To illustrate the effectiveness of our approach, we present a case study involving a generic linear time-invariant system and compare the results with a recently published algorithm.

Industrial controllers constitute the core of numerous automation solutions. Continuous control system operation is crucial in certain sectors, where hardware duplication serves as a strategy to mitigate the risk of unexpected operational halts due to hardware failures. Standby controller redundancy is a commonly adopted strategy for process automation. This approach involves an active primary controller managing the process while a passive backup is on standby, ready to resume control should the primary fail. Typically, redundant controllers are paired with redundant networks and devices to eliminate any single points of failure. The process automation domain is on the brink of a paradigm shift towards greater interconnectivity and interoperability. OPC UA is emerging as the standard that will facilitate this shift, with OPC UA PubSub as the communication standard for cyclic real-time data exchange. Our work investigates standby redundancy using OPC UA PubSub, analyzing a system with redundant controllers and devices in publisher-subscriber roles. The analysis reveals that failovers are not subscriber-transparent without synchronized publisher states. We discuss solutions and experimentally validate an internal stack state synchronization alternative. 

Distributed control systems are part of the often invisible backbone of modern society that provides utility services like water and electricity. Their uninterrupted operation is vital, and unplanned stops due to failure can be expensive. Critical devices, like controllers, are often duplicated to minimize the service stop probability, with a secondary controller acting as a backup to the primary. A seamless takeover requires that the backup has the primary's latest state, i.e., the primary has to replicate its state to the backup. While this method ensures high availability, it can be costly due to hardware doubling. This work proposes a state replication solution that doesn't require the backup to store the primary state, separating state storage from the backup function. Our replication approach allows for more flexible controller redundancy deployments since one controller can be a backup for multiple primaries without being saturated by state replication data. Our main contribution is the partible state replication approach, realized with a distributed architecture utilizing a consensus algorithm. A partial connectivity-tolerant consensus algorithm is also an additional contribution.

The efficiency of collaborative mobile robot applications is influenced by the inherent uncertainty introduced by humans' presence and active participation. This uncertainty stems from the dynamic nature of the working environment, various external factors, and human performance variability. The observed makespan of an executed plan will deviate from any deterministic estimate. This raises questions about whether a calculated plan is optimal given uncertainties, potentially risking failure to complete the plan within the estimated timeframe. This research addresses a collaborative task planning problem for a mobile robot serving multiple humans through tasks such as providing parts and fetching assemblies. To account for uncertainties in the durations needed for a single robot and multiple humans to perform different tasks, a probabilistic modeling approach is employed, treating task durations as random variables. The developed task planning algorithm considers the modeled uncertainties while searching for the most efficient plans. The outcome is a set of the best plans, where no plan is better than the other in terms of stochastic dominance. Our proposed methodology offers a systematic framework for making informed decisions regarding selecting a plan from this set, considering the desired risk level specific to the given operational context.

In order to facilitate the adoption of Time Sensitive Networking (TSN) by the industry, it is necessary to develop tools to integrate legacy systems with TSN. In this article, we propose a solution for the coexistence of different time domains from different legacy systems, each with its corresponding synchronization protocol, in a single TSN network. To this end, we experimentally identified the effects of replacing the communications subsystem of a legacy Ethernet-based network with TSN in terms of synchronization. Based on the results, we propose a solution called TALESS (TSN with Legacy End-Stations Synchronization). TALESS can identify the drift between the TSN communications subsystem and the integrated legacy devices (end-stations) and then modify the TSN schedule to adapt to the different time domains to avoid the effects of the lack of synchronization between them. We validate TALESS through both simulations and experiments on a prototype. We demonstrate that thanks to TALESS, legacy systems can synchronize through TSN and even improve features such as their reception jitter or their integrability with other legacy systems.