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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.

In this work, we address the problem of balancing an autonomous bicycle using direct data-driven control. Firstly, we demonstrate that a direct implementation of data-driven approaches may not guarantee reliable performance, and is highly dependent on how the parameters are selected. To address this issue, we make the reasonable assumption that we have access to some inaccurate information about the system. We use this inaccurate information to design a feedback linearization, based on a simplified point mass model of the bicycle, which does not accurately represent the dynamics of the system. Next, we suggest an inner and outer-loop control strategy. In the inner loop, we implement the aforementioned feedback linearization controller. Subsequently, in the outer loop, we consider the combination of the autonomous bicycle and the feedback controller as a black box, and we design a direct data-driven controller from acquired data. We use a SolidWorks model of a real autonomous bicycle to evaluate the performance of our proposed control approach and to compare it with the direct data-driven controller design derived from acquired data of the bicycle without feedback linearization. The results show that our proposed strategy significantly improves the performance and makes the data-driven control approach more reliable across a broader range of parameter choices compared to a data-driven controller designed based on data from the system without feedback linearization. Finally, we show that introducing an additional integral-like state further enhances the system's performance. 

This manuscript presents a novel distributed greedy framework applicable to a network of thermostatically controlled loads (TCLs) to desynchronize the network's aggregated power consumption. Compared to the existing literature, our proposed framework offers two distinct novelties. First, our proposed algorithm relaxes the restrictive assumptions associated with the communication graph among TCLs. To elaborate, our algorithm only requires a connected graph to execute control, a condition less demanding than its counterpart algorithms that mandate a star architecture, K-regular graphs, or undirected connected graphs. Second, a significant novel feature is the relaxation of the obligation to share private information, such as each unit's local power consumption and appliance temperatures, either with a central coordinator or neighboring TCLs. The findings presented in this brief are validated through simulations conducted over a network comprising 1000 TCLs.

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. 

The article discusses emerging non-von Neumann computer architectures and their integration in the computing continuum for supporting modern distributed applications, including artificial intelligence, big data, and scientific computing. It provides a detailed summary of available and emerging non-von Neumann architectures, which range from power-efficient single-board accelerators to quantum and neuromorphic computers. Furthermore, it explores their potential benefits for revolutionizing data processing and analysis in various societal, science, and industry fields. The article provides a detailed analysis of the most widely used class of distributed applications and discusses the difficulties in their execution over the computing continuum, including communication, interoperability, orchestration, and sustainability issues.

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.

A potential problem that may arise in the domain of distributed control systems is the existence of more than one primary controller in redundancy plans that may lead to inconsistency. An algorithm called NRP FD is proposed to solve this issue by prioritizing consistency over availability. In this paper, we demonstrate how by using modeling and formal verification, we discovered an issue in NRP FD where we may have two primary controllers at the same time. We then provide a solution to mitigate the identified issue, thereby enhancing the robustness and reliability of such systems.

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.