Aircraft systems manage resource sharing among several uniprocessor modules operating under the ARINC 653 specification. This standard, based on a two-level hierarchical architecture, guarantees function isolation within partitions. Partitions are scheduled on the processor according to an infinitely repeated offline statically defined cycle. During each cycle, a partition may be accessed either once or multiple times. This study focuses on the case where each partition is granted a single processor access per cycle. We propose an approach that involves transforming a two-level hierarchical scheduling model into a conventional uni-level system. This transformation enables the application of existing results from non-hierarchical uniprocessor scheduling for analysis. We establish a correlation between the schedulability properties of both systems and derive an exact schedulability criterion for fully harmonic task sets. Additionally, we extend this approach to compute precise worst-case response times of tasks within a two-level hierarchical system, leveraging established uniprocessor schedulability results. Finally, we evaluate the scalability of this method.

Discussions surrounding Industry 5.0 emphasize the need for a fully interconnected industrial ecosystem, integrating AI and digital twins. In this environment, industrial equipment must collaborate seamlessly with human workers, requiring low-latency, high-data-rate connectivity for real-time monitoring. To address this demand, Time-Sensitive Networking (TSN) standards have been developed. However, configuring TSN in dynamic industrial networks poses challenges. The IEEE 802.1Q standard offers mechanisms like the Time-Aware Shaper (TAS) to achieve deterministic latency when configured correctly. In this paper, we tackle the configuration of TAS in dynamic networks, like reconfiguration of production lines to fit production objectives or the deployment of new applications in the production line resulting in adding new flows in the network. Our solution employs Deep Reinforcement Learning (DRL), trained and evaluated through simulations, offering adaptability to changing network conditions and dynamic production line reconfigurations.

Industry 4.0 involves the networking of production equipment. This can be achieved thanks to the Time-Sensitive Networking (TSN) set of network standards. However, this new paradigm brings new challenges because TSN features optimization relies on the dynamic characteristics of the underlying communication network (e.g., network topology, routing strategy, critical flows requirements, etc.). This paper focuses on the case of the IEEE 802.1Qbv standard by exploring the applicability of a Deep Reinforcement Learning (DRL) approach in order to reduce the configuration time of the TSN-specific parameters, compared to exact or heuristic methods.

We demonstrate that, for periodic systems with offsets (tasks or flows of messages within a time-triggered network), the simulation cycle can be confined to the range of [0, hyperperiod) only under the condition that an idle point exists at the hyper-period. Furthermore, we establish that ensuring both (1) contention-freedom and (2) that no offset exceeds the value of the period minus duration, is a sufficient condition to guarantee the presence of an idle point at the hyper-period. Most contemporary methods aiming to eliminate latency are implicitly based on these properties and fail to propose a schedule if the input system does not allow a contention-free solution. Consequently, we propose a heuristic approach to scheduling periodic flows of frames within time-triggered networks. Our method focuses on minimizing latency, without necessitating a solution where every frame at every output port is contention-free, and it effectively manages the cyclicity problem.

In general computing systems, a job (process/task) may suspend itself whilst it is waiting for some activity to complete, e.g., an accelerator to return data. In real-time systems, such self-suspension can cause substantial performance/schedulability degradation. This observation, first made in 1988, has led to the investigation of the impact of self-suspension on timing predictability, and many relevant results have been published since. Unfortunately, as it has recently come to light, a number of the existing results are flawed. To provide a correct platform on which future research can be built, this paper reviews the state of the art in the design and analysis of scheduling algorithms and schedulability tests for self-suspending tasks in real-time systems. We provide (1) a systematic description of how self-suspending tasks can be handled in both soft and hard real-time systems; (2) an explanation of the existing misconceptions and their potential remedies; (3) an assessment of the influence of such flawed analyses on partitioned multiprocessor fixed-priority scheduling when tasks synchronize access to shared resources; and (4) a discussion of the computational complexity of analyses for different self-suspension task models.

Packet switched networks and message multiplexing have been a major upgrade for industrial systems communications. In the avionics domain, this evolution was brought by the introduction of avionics full duplex switched ethernet (AFDX). Guaranteed upper bounds of end-to-end delays for messages transmitted over an AFDX network are mandatory for certification reasons. In this article, we present the forward end-to-end delay analysis (FA), which is scalable to both large and heavily loaded configurations. A formal proof of FA is detailed and the approach is compared with alternative methods (network calculus and the trajectory approach) on two types of AFDX configurations (including a real industrial architecture).

Avionics Full DupleX Switched Ethernet (AFDX) switches, used in major civilian aircraft, have flow differentiation capability via multiple levels of Fixed Priority (FP) traffic classes. Inside a class, frames are handled in a First In First Out (FIFO) manner. End-to-end (ETE) delays, in such a network, have to be determined and an upper-bound is mandatory for certification purpose. Several methods, such as Network Calculus (NC) and Trajectory Approach (TA), can compute worst-case ETE delays in both FIFO and FP/FIFO contexts. The Forward end-to-end delay Analysis (FA) has been formerly developed for FIFO scheduling, and in this paper, we extend it to cover also FP/FIFO. We apply to the approach an optimization related to flow serialization, which has already proven to be effective for other methods and other servicing strategies. Finally, we apply FA on a sample configuration and compare the results to the two aforementioned approaches (TA and NC).

The AFDX (Avionics Full DupleX Switched Ethernet) is the backbone network of most modern civilian aircraft. For certification reasons, a guaranteed upper bound has to be determined on the end-to-end delay for all transmitted flows. Several approaches have been designed in order to analyze such delays. In this paper, we focus on the Forward End-to-End delays Analysis (FA). This method is based on real-time scheduling results. Our goal is to propose an improvement, that reduces the pessimism of the computed bounds, compared to earlier versions of the method. The improvement relies on the way the serialization of packets sharing a common link is taken into account. We also propose a comparative analysis on a simple configuration with two other methods (Network Calculus and the Trajectory approach).

AFDX (Avionics Full Duplex Switched Ethernet) is a network designed for safety-critical applications in avionics systems. The flow analysis is mandatory for the certification. The end-to-end (ETE) delay upper bound has to be computed and hence guaranteed. Different approaches have been proposed to compute ETE delay upper bound such as the Network Calculus and the trajectory method. Both methods are deemed to introduce some pessimism in the computations. We only focus next on the trajectory approach. This paper shows that the original trajectory approach is flawed and can compute an optimistic ETE. We present a counter-example and discuss the sources of optimism that must be considered to revise the trajectory method.

In this paper we propose parametric approximation algorithm (Fully Polynomial Time Approximation Scheme - FPTAS) that defines a compromise on the precision of computed worst-case response time upper bounds and the amount of extra processor speed required to achieve exact worst-case response times. Such a result fills the remaining gap between our previously published work [11] and also extends them to tasks with arbitrary relative deadlines. The method has been implemented and numerical results monitoring speedup factors are presented.

AFDX (Avionics Full DupleX Switched Ethernet, ARINC 664) developed for the Airbus A380 represents a major upgrade in both bandwidth and capability. Its reliance on Ethernet technology helps to lower some implementation costs, but guaranteed service presents challenges for system designers. An analysis of end-to-end transfer delays through the network is required in order to determine upper bounds. In this paper, we propose to compute probabilistic upper bounds for end-to-end delays on avionic flows. Such upper bounds can be exceeded with a given probability p, and are relevant in the context of avionics, where functions are designed to give accurate results even if they miss some frames. The stochastic network calculus approach analytically determines a probabilistic upper bound, whereas the simulation approach gives an experimental upper bound. The former may be used for new certification needs since it assures that the probability of exceeding the computed upper bound is not greater than p. The latter closely approximates actual network behavior and can help to give some idea of the pessimism of the stochastic network calculus upper bound. The two approaches have been developed in the context of an industrial AFDX network configuration.

AFDX (avionics full duplex switched Ethernet, AR-INC 664) developed for the Airbus A380 represents a major upgrade in both bandwidth and capability. Its reliance on Ethernet technology helps to lower some implementation costs, but guaranteed service presents challenges for system designers. An analysis of end-to-end transfer delays through the network is required in order to determine upper bounds. The stochastic network calculus approach analytically determines worst-case probabilistic upper bounds in the context of homogeneous avionics flows without priorities. Such upper bounds can be exceeded with a given probability PUB, and are relevant in the context of avionics, where functions are designed to give accurate results even if they miss some frames. Nowadays, there is a need to handle new classes of traffics with different priorities (voice, video, best-effort, ...) on the same AFDX network with no consequences on existing avionic flows. This paper presents the application of the stochastic network calculus approach in the context of a static priority queueing service discipline and evaluates the influence of the service discipline on analytical probabilistic upper bounds.

In most real-time systems, tasks use remote operations that are executed upon dedicated processors. External operations introduce self-suspension delays in the behavior of tasks. This paper presents several negative results concerning scheduling independent hard real-time tasks with self-suspensions. Our main objective is to show that well-known scheduling policies such as fixed-priority or earliest deadline first are not efficient to schedule such task systems. We prove the scheduling problem to be NP-hard in the strong sense, even for synchronous task systems with implicit deadlines. We also show that scheduling anomalies can occur at run-time: reducing the execution requirement or the suspension delay of a task can lead the task system to be infeasible under EDF. Lastly, we present negative results on the worst-case performances of well-known scheduling algorithms (EDFy RM, DM, LLF, SRPTF) to maximize tasks completed by their deadlines.