TO PROVIDE a standard real-time data network, the IEEE Time-Sensitive Networking (TSN) working group has defined several extensions to Ethernet. Among them, Cyclic Queuing and Forwarding (CQF), inspired by the "stop-and-go" queuing discipline [1] , has been introduced to provide bounded delay and limited jitter [2] . Briefly (see Section II ), CQF assumes a common time cycle T and uses two queues per output port to alternately store and forward packets. It guarantees that the delay experienced by a packet traversing h nodes lies between ( h −1) T and ( h +1) T .
Modern real-time systems require accurate characterization of task timing behavior to ensure predictable performance, particularly on complex hardware architectures. Existing methods, such as worst-case execution time analysis, often fail to capture the fine-grained timing behaviors of a task under varying resource contexts (e.g., an allocation of cache, memory bandwidth, and CPU frequency), which is necessary to achieve efficient resource utilization. In this paper, we introduce a novel generative profiling approach that synthesizes context-dependent, fine-grained timing profiles for real-time tasks, including those for unmeasured resource allocations. Our approach leverages a nonparametric, conditional multi-marginal Schrödinger Bridge (MSB) formulation to generate accurate execution profiles for unseen resource contexts, with maximum likelihood guarantees. We demonstrate the efficiency and effectiveness of our approach through real-world benchmarks, and showcase its practical utility in a representative case study of adaptive multicore resource allocation for real-time systems.
Deadline misses are more common in real-world systems than one may expect. The weakly-hard task model has become a standard abstraction to describe and analyze how often these misses occur, and has been especially used in control applications. Most existing control approaches check whether a controller manages to stabilize the system it controls when its implementation occasionally misses deadlines. However, they usually do not incorporate deadline-overrun knowledge during the controller synthesis process. In this paper, we present a framework that explicitly integrates weakly-hard constraints into the control design. Our method supports various overrun handling strategies and guarantees stability and performance under weakly-hard constraints. We validate the synthesized controllers on a Furuta pendulum, a representative control benchmark. The results show that constraint-aware controllers significantly outperform traditional designs, demonstrating the benefits of proactive and informed synthesis for overrun-aware real-time control.
With the increasing popularity of electric vehicles (EVs), drivers want their vehicle batteries to be charged in a few minutes; at present, this is feasible only if battery service stations replace the EV battery pack with a fully charged battery pack. In this paper, we formulate the scheduling problem for battery swap stations, aiming to provide the drivers timing guarantees for different types of EVs, each with its own sporadic/periodic arrival pattern and deadline constraint. To solve this problem, we analyze its unique characteristics from a real-time scheduling perspective, with the main challenge being the circular timing dependency between two distinct scheduling processes: the swapping operation and the charging operation. We first derive a sufficient condition that decouples the dependency and then develop scheduling policies and timing guarantee techniques, designed for not only being specialized for the problem but also accommodating the sufficient condition in a time-predictable and resource-efficient manner. While the problem formulation and solution hold significance as the first attempt to establish real-time scheduling principles for battery swap stations, we also address how to accommodate real-world EV arrivals at a swapping station that do not necessarily follow a sporadic/periodic pattern. Finally, we evaluate the effectiveness of the proposed principles not only in addressing the formulated problem but also in accommodating real-world EV arrival patterns via simulation and a case study.
The Robot Operating System 2 (ROS 2) is a widely used middleware that provides software libraries and tools for developing robotic systems. In these systems, tasks are scheduled by ROS 2 executors. Since the scheduling behavior of the default ROS 2 executor is inherently different from classical real-time scheduling theory, dedicated analyses or alternative executors requiring substantial changes to ROS 2 have been developed. In 2023, the events executor was introduced into ROS 2. It features an events queue and allows the possibility to make scheduling decisions immediately after a job is completed. In this paper, we show that with minor modifications of the events executor, a large body of research results from classical real-time scheduling theory becomes directly applicable to ROS 2. This enables analytical bounds on the worst-case response time and the end-to-end latency, outperforming bounds for the default ROS 2 executor in many scenarios. Our solution is easy to integrate into existing ROS 2 systems since it requires only minor modifications of the events executor, which is natively included in ROS 2. The evaluation results show that our ROS 2 events executor with minor modifications can have significant improvement in terms of dropped jobs, worst-case response time, end-to-end latency, and performance compared to the default ROS 2 executor.
