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Tight end-to-end worst-case delay bounds for periodic traffic streams are essential for time sensitive networks. In this paper, we provide an algorithm to compute a tight (and accurate) end-to-end worst-case bound by considering distinct topological patterns and the manner in which streams enter and leave switches. This refined analysis uses non-preemptive, strict-priority arbitration mechanism commonly deployed in Ethernet switches. Compared to the state-of-the-art that considers all higher and equal priority interference as contributing to the worst-case bound, we present an analytical approach for computing a tighter worst-case delay bound and prove through discrete event simulations that only a certain number of equal-priority interference streams can actually affect the worst-case case. Our results enable efficient resource allocation and have implications for online re-configuration mechanisms for time-sensitive factory communication systems.

Time Sensitive Networking (TSN) is a set of standards aimed at providing real-time guarantees over existing Ethernet standards. Worst-case traversal time (WCTT) analyses of network traffic are traditionally used in schedulability and routing analyses to determine feasible routes for traffic streams. However, worst-case conditions happen quite rarely from a probabilistic perspective. The typical or average-case traversal times are easier to compute and can be used as an effective design tool for routing and scheduling. In this paper, we present "MaLoC"or Maximally Loaded Common links for routing and scheduling of rate-constrained (RC) traffic in time-sensitive networks (TSN). The proposed framework employs a fully decentralized approach to route and schedule generation with only switch-local information. We further provide a preliminary evaluation of the proposed approach using a simple network topology.

Within the Computer Science community, many ethical issues have emerged as significant and critical concerns. Computer ethics is an academic field in its own right and there are unique ethical issues associated with information technology. It encompasses a range of issues and concerns including privacy and agency around personal information, Artificial Intelligence and pervasive technology, the Internet of Things and surveillance applications. As computing technology impacts society at an ever growing pace, there are growing calls for more computer ethics content to be included in Computer Science curricula. In this paper we present the results of a survey that polled faculty from Computer Science and related disciplines about teaching practices for computer ethics at their institutions. The survey was completed by respondents from 61 universities across 23 European countries. Participants were surveyed on whether or not computer ethics is taught to Computer Science students at each institution, the reasons why computer ethics is or is not taught, how computer ethics is taught, the background of staff who teach computer ethics and the scope of computer ethics curricula. This paper presents and discusses the results of the survey.

In real-time systems that employ preemptive scheduling and cache architecture, it is essential to account as precisely as possible for cache-related preemption delays in the schedulability analysis, as an imprecise estimation may falsely deem the system unschedulable. In the current state of the art for preemptive scheduling of tasks with fixed preemption points, the existing schedulability analysis considers overly pessimistic estimation of cache-related preemption delay, which eventually leads to overly pessimistic schedulability results. In this paper, we propose a novel response time analysis for real-time tasks with fixed preemption points, accounting for a more precise estimation of cache-related preemption delays. The evaluation shows that the proposed analysis significantly dominates the existing approach by being able to always identify more schedulable tasksets.

Schedulability analyses for preemptive real-time systems need to take into account cache-related preemption delays (CRPD) caused by preemptions between the tasks. The estimation of the CRPD values must be sound, i.e. it must not be lower than the worst-case CRPD that may occur at runtime, but also should minimise the pessimism of estimation. The existing methods over-approximate the computed CRPD upper bounds by accounting for multiple preemption combinations which cannot occur simultaneously during runtime. This over-approximation may further lead to the over-approximation of the worst-case response times of the tasks, and therefore a false-negative estimation of the system’s schedulability. In this paper, we propose a more precise cache-aware response time analysis for sporadic real-time systems under fully-preemptive fixed priority scheduling. The evaluation shows a significant improvement over the existing state of the art approaches.

The increasing industrial demand for handling complex functionalities has influenced the design of hardware architectures for time critical embedded systems, during the past decade. Multi-core systems facilitate the inclusion of many complex functionalities, while, at the same time, inducing cache related overheads, as well as adding partitioning complexity to the overall system schedulability. One of the efficient paradigms for controlling and reducing the cache related costs in real-time systems is Limited Preemptive Scheduling (LPS), with its particular instance Fixed Preemption Points Scheduling (LP-FPPS), that has been shown to outperform other alternatives as well as has been supported by and investigated in the automotive domain. With respect to the partitioning constraints, Partitioned Scheduling has been widely used to pre-runtime allocate tasks to specific cores, resulting in a predictable cache-related preemption delays estimations. In this paper we propose to integrate LP-FPPS and Partitioned Scheduling on fixed-priority multicore real-time systems in order to increase the overall system schedulability.We define a new joint approach for task partitioning and preemption point selection, that is based on the computation of the maximum blocking tolerance upon each allocation, thus being able to quantify the schedulability of the taskset on each processor. Furthermore, we investigate partitioning strategies based on different heuristics, i.e. First Fit Decreasing and Worst Fit Decreasing, and priority and density taskset orderings. The evaluation performed on randomly generated tasksets shows that in the general case, no single partitioning strategy fully dominates the others. However, the evaluation results reveal that certain partitioning strategies perform significantly better with respect to the overall schedulability for specific taskset characteristics. The results also reveal that the proposed partitioning strategies outperform Fully Preemptive and Non-Preemptive partitioned scheduling in terms of successful partitioning.

Time sensitive networking (TSN) is gaining attention in industrial automation networks since it brings essential real-time capabilities at the data link layer. Though it can provide deterministic latency under error free conditions, TSN still largely depends on space redundancy for improved reliability. In many scenarios, time redundancy could be an adequate as well as cost efficient alternative. Time redundancy in turn will have implications due to the need for over-provisions needed for timeliness guarantees. In this paper, we discuss how to embed fault-tolerance capability into TSN schedules and describe our approach using a simple example.

Fixed priority scheduling is used in many real-time systems; however, both preemptive and non-preemptive variants (FP-P and FP-NP) are known to be sub-optimal when compared to an optimal uniprocessor scheduling algorithm such as preemptive Earliest Deadline First (EDF-P). In this paper, we investigate the sub-optimality of xed priority non-preemptive scheduling. Speci cally, we derive the exact processor speed-up factor required to guarantee the feasibility under FP-NP (i.e. schedulablability assuming an optimal priority assignment) of any task set that is feasible under EDF-P. As a consequence of this work, we also derive a lower bound on the sub-optimality of non-preemptive EDF (EDF-NP). As this lower bound matches a recently published upper bound for the same quantity, it closes the exact sub-optimality for EDF-NP. It is known that neither preemptive, nor non-preemptive xed priority scheduling dominates the other, in other words, there are task sets that are feasible on a processor of unit speed under FP-P that are not feasible under FP-NP and vice-versa. Hence comparing these two algorithms, there are non-trivial speedup factors in both directions. We derive the exact speed-up factor required to guarantee the FP-NP feasibility of any FP-P feasible task set. Further, we derive the exact speed-up factor required to guarantee FP-P feasibility of any constrained-deadline FP-NP feasible task set.

Preemption point selection has a significant impact on the schedulability of Real-Time tasks under the Fixed Preemption Point approach in Limited Preemptive Scheduling. Many real time systems can occasionally tolerate deadline misses as long as their occurrence does not exceed a specified probabilistic threshold. However, the existing approaches for preemption point selection are inappropriate for such systems, as they are mainly aiming to provide hard guarantees, considering worst case (upper bounded) preemption overheads. Additionally, the worst case preemption overheads typically occur with very low probabilities. In this paper, we propose a novel preemption point selection approach, and an associated probabilistic response time analysis, considering preemption related overheads modelled as probabilistic distributions. The method is suitable for providing solutions in systems that can occasionally tolerate deadline misses and can be interesting in the context of mixed criticality systems. Our method is able to find solutions, in terms of preemption point selections, in all cases where the existing approaches do. Moreover, it provides preemption point selections for additional tasksets that guarantees the overall taskset schedulability with a certain probability. The evaluation results show an improvement with respect to increasing the number of tasksets for which a preemption point selection is possible compared to existing, upper-bound based, selection approaches. The results show that the deadline miss probabilities of the tasksets and associated preemption point selections are considerably low.

Internet of Things (IoT) has revolutionized transportation systems by connecting vehicles consequently enabling their tracking, as well as monitoring of driver activities. Such an IoT platform requires a significant amount of data to be send from the on-board vehicle to the off-board servers, contributing to high network usage. The data can be send at regular intervals or in an event-based manner whenever relevant events occur. In interval-based approach, the data is send even if it is not relevant for reporting leading to a wastage of network resources, e.g., when the data does not change considerably compared to the previously sent value. In this paper, we investigate the possibility of using an event-based architecture to send data from the on-board system to the off-board system. The results show that our event-based architecture improves the accuracy of data available at the off-board system, by a careful selection of events. Moreover, we found that our event based architecture significantly decreases the frequency of sending messages, particularly during highway driving, leading to reduced average data transfer rates. Our results enable a customer to perform trade-offs between accuracy and data transfer rates.