real-time scheduling

ROS 2 lacks systematic support for real-time capabilities, hindering its applicability in high-determinism robotic systems. This work presents the first comprehensive taxonomy focused on real-time performance in ROS 2, integrating multidimensional research aspects including scheduling mechanisms, communication latency modeling based on DDS, multi-threaded executor design, hardware co-design (encompassing micro-ROS and GPU real-time management), and performance profiling tools. By establishing a unified evaluation framework grounded in key metrics such as response time and data timeliness, the study systematically reviews existing approaches, clarifies the trajectory of technical evolution, and offers developers a clear optimization roadmap. The proposed framework aims to advance the ROS community’s progress toward robust real-time robotic systems.

To address the high scheduling overhead caused by nested scheduling (OS threads + middleware Executors) in commercial real-time systems such as ROS 2, this paper proposes a lightweight, real-time scheduling paradigm—“one-to-one binding of callbacks to OS threads”—which bypasses the middleware scheduling layer and enables native OS-level scheduling control at the callback granularity. Our key contributions are: (1) the first middleware-transparent callback scheduling model, eliminating nested scheduling complexity; and (2) CallbackIsolatedExecutor, a novel executor that supports direct configuration of kernel-level parameters—including SCHED_FIFO, priority, and CPU affinity. Experimental results show that, compared to MultiThreadedExecutor, our approach significantly reduces context switches, user-to-kernel transitions, and memory overhead. Against SingleThreadedExecutor, inter-process and intra-process communication latencies remain consistently at 1.4× and 5×, respectively—achieving a balanced trade-off between real-time determinism and schedulability control.

This work addresses the critical need for rigorous assurance that implementations of Earliest Deadline First (EDF) schedulers in real-time operating systems adhere precisely to their intended scheduling semantics, rather than relying solely on abstract analyses. The paper presents the first general, property-driven formal verification framework specifically tailored for EDF implementations, explicitly defining three core correctness properties and conducting deductive verification using the Frama-C/ACSL platform. The framework has been successfully applied to EDF extensions in RTEMS 5, RTEMS 6, and FreeRTOS, demonstrating that three architecturally distinct scheduler implementations all satisfy the essential correctness properties. This approach effectively resolves the semantic fidelity challenges arising from reusing fixed-priority kernel infrastructure and substantially enhances the trustworthiness of EDF scheduler implementations in safety-critical systems.

This paper addresses the lack of clarity regarding the diversity and evolutionary trajectories of modern workload schedulers. We propose a cross-layer taxonomy comprising three categories: OS process scheduling, cluster job scheduling, and big-data scheduling. Through algorithmic feature analysis and historical comparative study, we systematically characterize the design rationales, optimization objectives, and technological evolution of these schedulers, uncovering shared design patterns across local and distributed environments. Our key contribution is the first unified classification framework, which identifies three fundamental differentiating dimensions: resource abstraction granularity, scheduling timing, and feedback mechanism. Based on this analysis, we distill general-purpose scheduling design principles targeting heterogeneity, scalability, and QoS guarantees. The study provides both theoretical foundations and practical guidance for scheduler selection, cross-layer coordination optimization, and next-generation scheduler architecture design.

To address the lack of systematic response-time analysis and real-time scheduling guarantees in ROS 2’s multi-threaded executor, this paper introduces the first response-time analysis framework tailored to its kernel-level execution semantics. The framework supports modeling of arbitrary- and constrained-deadline task chains and precisely captures mutual exclusion among callback groups. We further propose a priority-driven scheduling enhancement mechanism that optimizes critical-path response times while preserving schedulability. Experimental evaluation on the Jetson AGX Xavier platform demonstrates that our framework yields tighter safe upper bounds on response time, reduces average response time of critical chains by a significant margin, and improves overall system schedulability by 23.6%.

HPC workloads are becoming increasingly heterogeneous, rendering traditional static heuristic schedulers inadequate for dynamic resource demands. To address this, we propose SchedTwin—the first real-time digital twin system for HPC job scheduling. It continuously ingests runtime event streams to drive high-fidelity discrete-event simulation, enabling rapid online evaluation of “what-if” scenarios across multiple scheduling policies and facilitating goal-driven, closed-loop adaptive scheduling. Deeply integrated with the PBS scheduler, SchedTwin achieves low-overhead (sub-10-second decision latency) and high-accuracy online policy optimization. Experimental evaluation in production environments demonstrates that SchedTwin significantly outperforms mainstream static schedulers—overcoming the longstanding dual bottlenecks of adaptability and timeliness inherent in conventional HPC scheduling approaches.

This work addresses the challenges in industrial scheduling where asynchronous event streams often lead to inconsistent decision states, ambiguous action validity, and difficulties in attributing execution errors in reinforcement learning policies. To resolve these issues, the paper proposes a policy-decoupled execution and measurement layer that bridges the policy and the execution environment. By constructing valid decision snapshots, defining standardized execution contracts, and recording multidimensional execution deviations, the approach structurally formalizes execution semantics for the first time. This enables observable and attributable deployment discrepancies between simulation and reality, transforming ambiguous execution failures into type-labeled supervisory signals. Experimental results demonstrate that the framework consistently enhances diagnostic capability across varying observation delays, significantly reducing avoidable errors under low-latency conditions and providing structured supervisory data for policy evaluation and optimization.

Distributed time-sensitive systems struggle to simultaneously ensure timeliness and consistency under communication delays and synchronization uncertainty. This work proposes the maxwait mechanism, which integrates classical approaches—including PTIDES, Chandy-Misra, Time-Warp, and Lamport-style failure detection—within a unified semantic framework through an explicitly configurable coordination policy. The mechanism provides bounded-time failure detection, determinism guarantees, and real-time behavior support for paradigms such as logical execution time, CRDTs, and Actors. Implemented as an extension of Lingua Franca, the approach ensures logical time consistency when communication delays remain within prescribed bounds and offers structured fault tolerance when they are exceeded, thereby significantly enhancing temporal control and robustness in multi-paradigm distributed systems.

Existing heterogeneous accelerator designs primarily prioritize throughput or quality of service, falling short in meeting the stringent requirements of safety-critical real-time systems—namely, predictability, real-time awareness, and rigorous schedulability. This work proposes PHAROS, a novel framework that, for the first time, integrates modern real-time scheduling theory into heterogeneous accelerator design. PHAROS introduces a preemptive scheduling mechanism supporting both FIFO and EDF policies and formulates a soft real-time schedulability analysis model. Building upon this foundation, it develops a schedulability-driven design space exploration algorithm. Experimental results demonstrate that PHAROS significantly improves task set schedulability and real-time responsiveness across diverse applications, uncovering a substantially broader range of feasible configurations compared to throughput-oriented approaches.

This work addresses the lack of cross-DAG priority-aware scheduling in ROS 2’s default executors, which leads to callback contention, priority inversion, and deadline instability—challenges that hinder the deployment of ROS 2 in safety-critical real-time systems. To overcome these limitations, we propose ReDAG-RT, a userspace global scheduling framework that, for the first time in ROS 2, enables deterministic execution with cross-DAG priority guarantees and concurrency control, without requiring modifications to the ROS 2 API or the underlying OS scheduler. ReDAG-RT integrates rate-monotonic theory through a rate-priority-driven global ready queue, per-DAG concurrency boundary enforcement, and recursive response-time analysis. Experimental results demonstrate that ReDAG-RT reduces deadline miss rates by 29.7% and 99th-percentile response times by 42.9% compared to native executors, while asymmetric concurrency boundaries further decrease interference by 40.8%.

Dynamic flexible job shop scheduling faces the challenge of simultaneously achieving millisecond-level real-time responsiveness and long-term global optimization. To address this, this work proposes RACE-Sched, a framework featuring an asynchronous dual-stream architecture that decouples execution from reasoning: a reactive stream employs low-latency symbolic heuristics for immediate scheduling decisions, while a deliberative stream leverages large language models to parallelly generate, validate, and evolve scheduling rules. The framework innovatively incorporates a semantic rule repository to enable cross-scale transferability and integrates sandbox validation with atomic update mechanisms to ensure system safety. Experimental results demonstrate that RACE-Sched significantly outperforms existing deep reinforcement learning and LLM-based approaches on GEN-Bench, MK-Bench, and JMS-Bench, achieving superior performance in both scheduling quality and dynamic adaptability.