Self-suspension behavior happens when a job has to wait for some activity to complete and results in substantial schedulability degradation in real-time systems. Despite extensive studies for self-suspending real-time task systems, the state of the art has barely addressed the optimality of the scheduling algorithms, especially for tasks with dynamic self-suspension. In this paper, we explore optimal priority assignment for periodic real-time tasks with dynamic self-suspension under Task-level Fixed-Priority (T-FP) scheduling. To that end, we provide exact schedulability tests for frame-based and synchronous harmonic tasks. We show that the Suspension-Aware Deadline-Monotonic (SADM) priority assignment is an optimal fixed-priority scheduler for many scenarios. Further, for cases where SADM is not optimal, we adopt Audsley's Optimal Priority Assignment (OPA) approach to derive an optimal fixedpriority assignment. Evaluation results show that the exact tests outperform state-of-the-art schedulability tests from the literature, and that optimal priority assignments significantly improve schedulability over classical priority assignments.

Cyber-physical systems (CPS) must interact with varying environments at fine-grained time-scales, assuring control safety and stability while optimizing application-specific performance objectives. To address those requirements, co-design of real-time control and scheduling has received considerable attention over multiple decades, to allow rigorous assurance of system properties while enabling diverse forms of adaptation to changing operating conditions. In this paper, we present a new formalization of the periodicity requirements for control inputs to (1) guarantee reachability of safe (and avoidance of unsafe) portions of the system state space, (2) adaptively manage dynamic periodicity constraints that may change as the state space is traversed, and (3) express minimum periods to enable safe hand-offs between high-performance controllers and more conservative backup controllers. Our evaluations of this approach confirm that it is able to maintain system safety and stability while optimizing system performance.

Timely observation of transient astrophysical phenomena (TAP) is of crucial importance for our understanding of the universe and the laws of physics, as recognized by the National Academies in the Astro2020 decadal survey. Ultimately, the goal is to observe TAPs as early as possible using optical telescopes. This is non-trivial due to the probabilistic nature of the search problem, where multiple potential sky locations for a TAP, each with an associated probability, must be scheduled for observation before successful localization. The problem lies at the intersection of several research disciplines, including realtime systems, cyber-physical systems, astrophysics, and operations research, motivating the need for a unified modeling framework. To this end, we introduce the first formal stochastic, response-time-aware model for search planning toward detection and localization of TAPs. We consider the problem of maximizing expected utility of early localization and show that it is reducible to the Orienteering Problem. Building on this formulation, we develop the real-time-capable Greedy-Christofides Pathfinding (GCP) algorithm. An evaluation on 37 probability maps from LIGO demonstrates that GCP consistently achieves high solution quality and computational efficiency across diverse search scenarios. GCP achieves$\leq 0.5 \%$deviation from the ILP-computed optimal solution on tractable problem instances while running within a second, on average, for larger inputs.

Abstract We present the first (to our knowledge) Deep-Learning based framework for real-time schedulability-analysis that guarantees to never incorrectly mis-classify an unschedulable system as being schedulable, and is hence suitable for use in safety-critical scenarios. We relate applicability of this framework to well-understood concepts in computational complexity theory: membership in the complexity class NP. We apply the framework upon the widely-studied schedulability analysis problems of determining whether a given constrained-deadline sporadic task system is schedulable on a preemptive uniprocessor under both Deadline-Monotonic and EDF scheduling. As a proof-of-concept, we implement our framework for Deadline-Monotonic scheduling, and demonstrate that it has a predictive accuracy exceeding $$70\%$$ 70 % for systems of as many as 20 tasks without making any unsafe predictions . Furthermore, the implementation has very small ( $$<1$$ < 1 ms on two widely-used embedded platforms; $$<4~\upmu$$ < 4 μ s on an embedded FPGA) and highly predictable running times.

FPGAs are widely deployed on high-energy astrophysics telescopes to preprocess and reduce sensor data read out by front-end electronics. Across instruments, these computational pipelines have similar semantics, sharing common stages such as pedestal subtraction, signal integration, zero-suppression, island detection, and centroiding. However, diverse telescope designs require unique implementations of these algorithms, and the logic is often rewritten from scratch for a new instrument.

Elastic scheduling is a framework to reduce task utilizations (often by increasing periods) in response to system overload. This paper extends elastic scheduling to uniprocessor scheduling of implicit-deadline task sets for which periods must remain harmonic. We argue that for tasks with periods constrained to continuous intervals, the problem of selecting harmonic periods from those intervals is unlikely to have a polynomial time solution. However, we outline an approach that is pseudo-polynomial in the range of acceptable periods. We then show that the problem of elastic scheduling is NP-hard with harmonic constraints. Nonetheless, if a total order is imposed on task periods (a natural restriction in many applications with execution pipelines that synchronize input data sources), the problem can be reduced offline to a lookup table, enabling polynomial-time online adaptation if available CPU bandwidth changes. We implement the proposed algorithm in two real-world applications: the Fast Integrated Mobility Spectrometer (FIMS) and ORB-SLAM3. We demonstrate that elastic scheduling allows FIMS to adjust its execution to avoid missing deadlines on a SWaP-constrained computational platform, and that it improves ORB-SLAM3's localization results by as much as lO.4x when available CPU bandwidth changes dynamically during runtime.

Buttazzo et al.’s elastic scheduling model allows task utilizations to be “compressed” to ensure schedulability atop limited resources. Each task is assigned a range of acceptable utilizations and an “elastic constant” representing the relative adaptability of its utilization. In this paper, we consider federated scheduling, under which each high-utilization parallel task is assigned dedicated processor cores. We propose a new model of elastic workload compression for parallel DAG tasks that assigns each subtask its own elastic constant and continuous range of acceptable workloads. We show that the problem can be solved offline as a mixed-integer quadratic program, or online using a pseudo-polynomial dynamic programming algorithm. We also consider joint core allocation and compression of low-utilization sequential tasks and present a mixed-integer linear program for optimal elastic compression of tasks under partitioned EDF scheduling. We show empirical improvements in schedulability over the prior work and present a case study for the Fast Integrated Mobility Spectrometer (FIMS).

Elastic scheduling provides a model for systems in which individual task utilizations can adapt to guarantee schedulability despite limited resources. Each task is characterized by a range of acceptable utilizations and an “elastic constant” representing its flexibility to reduce or “compress” its utilization from the desired maximum. Utilization compression is realized by either extending task periods or reducing workloads. This paper extends the model to address period compression for fixed-priority constrained-deadline task systems scheduled on a uniprocessor. We propose two approximate algorithms and one optimal algorithm for determining compression under the model. We then compare the execution times and accuracies of all three, demonstrating that even for large task sets, online compression can be performed feasibly on low-powered embedded systems.

Many real-time systems run in dynamic environments where exogenous factors inform task workloads and deadlines, which may not be known prior to job release. A job of a task that would otherwise miss its deadline may adapt to remain schedulable by executing in a degraded state that reduces its workload. We suggest that such a task should adjust parameters of its computation over multiple dimensions to maintain schedulability while minimizing loss of utility, which we discuss for highly parallel fork-join tasks executing on a fixed number of dedicated processors. We identify the parameterized degrees of freedom over which workload can be adjusted, then characterize the impact of workload reduction on response time and utility. From this, we generate a Pareto-optimal surface over which efficient search, interpolation, and extrapolation enable online selection of task parameters at time of job release. We apply this approach to the Advanced Particle-astrophysics Telescope, a planned mission to perform real-time gamma-ray burst (GRB) localization using SWaP-constrained embedded hardware aboard an orbiting platform. Due to GRBs' dynamic and uncertain nature, the workload and deadline may not be known prior to job release. Nonetheless, even for bright GRBs that may otherwise take longer than a second to localize on candidate embedded hardware, our approach often enables sub-degree accuracy while meeting a 33 ms imposed deadline.

Component-based design can encapsulate and isolate state and the operations on it, but timing semantics crosscut these boundaries when a real-time task’s control flow spans multiple components. Under priority-based scheduling, inter-component control flow should be coupled with priority information, so that task execution can be prioritized appropriately end-to-end. However, the CAmkES component architecture for the seL4 microkernel does not adequately support priority propagation across intercomponent requests: component interfaces are bound to threads that execute at fixed priorities provided at compile-time in the component specification. In this paper, we present a new library for CAmkES with a thread model that supports (1) multiple concurrent requests to the same component endpoint; (2) propagation and enforcement of priority metadata, such that those requests are appropriately prioritized; and (3) implementations of Non-Preemptive Critical Sections, the Immediate Priority Ceiling Protocol and the Priority Inheritance Protocol for components encapsulating critical sections of exclusive access to a shared resource. We measure overheads and blocking times for these new features and use existing theory to perform schedulability analysis. Evaluations on both Intel x86 and ARM platforms show that our new library allows CAmkES to provide suitable end-to-end timing for real-time systems.

As cyber-physical systems have become increasingly complex, rising computational demand has led to the ubiquitous use of multicore processors in embedded environments. Size, Weight, Power, and Cost (SWaP-C) constraints have pushed more processes onto shared platforms, including real-time tasks with deadline requirements. To prevent temporal interference among tasks running concurrently or in parallel in such systems, many operating systems provide priority-based scheduling and enforce processor reservations based on Worst-Case Execution Time (WCET) estimates. However, shared resources (both architectural components and data structures within the operating system) provide channels through which these constraints can be broken. Prior work has demonstrated that malicious execution by one or more processes can cause significant delays, leading to potential deadline misses in victim tasks. In this paper, we introduce PolyRhythm, a three-phase attack template that combines primitives across multiple architectural and kernel-based channels: (1) it uses an offline genetic algorithm to tune attack parameters based on the target hardware and OS platform; then (2) it performs an online search for regions of the attack parameter space where contention is most likely; and finally (3) it runs the attack primitives, using online reinforcement learning to adapt to dynamic execution patterns in the victim task. On a representative platform (Raspberry Pi 3B) Poly Rhythm outperforms prior work, achieving significantly more slowdown. As we show for several hardware/software platforms, Poly Rhythm also allows us to characterize the extent to which interference can occur; this helps to inform better estimates of execution times and overheads, towards preventing deadline misses in real-time systems.