In real-time systems optimization, designers often face a challenging problem posed by the non-convex and non-continuous schedulability conditions, which may even lack an analytical form to understand their properties. To tackle this challenging problem, we treat the schedulability analysis as a black box that only returns true/false results. We propose a general and scalable framework to optimize real-time systems with continuous variables, named Numerical Optimizer with Real-Time Highlight (NORTH). NORTH is built upon the gradient-based active-set methods from the numerical optimization literature but with new methods to manage active constraints for the non-differentiable schedulability constraints. In addition, we also generalize NORTH to NORTH+ to collaboratively optimize priority assignments, a common type of discrete variables, with continuous variables based on numerical optimization algorithms. We demonstrate the algorithm performance with two example applications: energy minimization based on dynamic voltage and frequency scaling (DVFS), and optimization of control system performance. In these experiments, NORTH are 10 2 to 10 5 times faster than state-of-the-art methods while maintaining similar or better solution quality. NORTH+ outperforms NORTH by 30% with similar algorithm scalability. Both NORTH and NORTH+ support black-box schedulability analysis, ensuring broad applicability.

The Logical Execution Time (LET) programming model has recently received considerable attention, particularly because of its timing and dataflow determinism. In LET, task computation appears always to take the same amount of time (called the task's LET interval), and the task reads (resp. writes) at the beginning (resp. end) of the interval. Compared to other communication mechanisms, such as implicit communication and Dynamic Buffer Protocol (DBP), LET performs worse on many metrics, such as end-to-end latency (including reaction time and data age) and time disparity jitter. Compared with the default LET setting, the flexible LET (fLET) model shrinks the LET interval while still guaranteeing schedulability by introducing the virtual offset to defer the read operation and using the virtual deadline to move up the write operation. Therefore, fLET has the potential to significantly improve the end-to-end timing performance while keeping the benefits of deterministic behavior on timing and dataflow. To fully realize the potential of fLET, we consider the problem of optimizing the assignments of its virtual offsets and deadlines. We propose new abstractions to describe the task communication pattern and new optimization algorithms to explore the solution space efficiently. The algorithms leverage the linearizability of communication patterns and utilize symbolic operations to achieve efficient optimization while providing a theoretical guarantee. The framework supports optimizing multiple performance metrics, and guarantees bounded suboptimality when optimizing end-to-end latency. Experimental results show that our optimization algorithms improve upon the default LET and its existing extensions and significantly outperform implicit communication and DBP in terms of various metrics, such as end-to-end latency, time disparity, and its jitter.

Modern real-time systems often involve numerous computational tasks characterized by intricate dependency relationships. Within these systems, data propagate through cause–effect chains from one task to another, making it imperative to minimize end-to-end latency to ensure system safety and reliability. In this article, we introduce innovative nonpreemptive scheduling techniques designed to reduce the worst-case end-to-end latency and/or time disparity for task sets modeled with directed acyclic graphs (DAGs). This is challenging because of the noncontinuous and nonconvex characteristics of the objective functions, hindering the direct application of standard optimization frameworks. Customized optimization frameworks aiming at achieving optimal solutions may suffer from scalability issues, while general heuristic algorithms often lack theoretical performance guarantees. To address this challenge, we incorporate the “1-opt” concept from the optimization literature (Essentially, 1-opt means that the quality of a solution cannot be improved if only one single variable can be changed) into the design of our algorithm. We propose a novel optimization algorithm that effectively balances the tradeoff between theoretical guarantees and algorithm scalability. By demonstrating its theoretical performance guarantees, we establish that the algorithm produces 1-opt solutions while maintaining polynomial run-time complexity. Through extensive large-scale experiments, we demonstrate that our algorithm can effectively reduce the latency metrics by 20% to 40%, compared to state-of-the-art methods.

In the optimization of real-time systems, designers often face a challenging problem where the schedulability conditions are non-convex, non-continuous, or lack an analytical form to understand their properties. In this paper, we propose a general and scalable framework for optimizing real-time systems, named Numerical optimizer with Real-Time Highlight (NORTH). NORTH treats schedulability analysis as a blackbox which may only return true/false results on system schedulability. Built upon the active-set methods from the gradient-based numerical optimization literature, NORTH proposes new methods to manage active constraints to further improve the gradient-based optimizers. We apply the proposed approach to two example problems, one on energy optimization for systems with dynamic voltage and frequency scaling, and the other on the optimization of control performance. Experimental results demonstrate that the proposed framework runs 102to 105times faster than state-of-the-art methods while maintaining similar solution quality.

Equipping real-time systems with soft error resilience can be challenging due to the tradeoff of the timing and failure requirements for mixed-criticality tasks. Violation of these requirements yields failed task scheduling in one way or another. However, not every task requires the same degree of soft error resilience. For example, low-criticality tasks can run with low or even no soft error resilience, whereas mid- or highcriticality tasks may require relatively high resilience depending on their inherent failure requirement. Unfortunately, existing soft error resilience schemes do not have the ability to control the degree of their resilience in a fine-grained way, i.e., they can only be turned on or off as a whole during task execution. To this end, this paper presents RTailor (Resilience Tailor), a compiler-directed parameterized soft error resilience scheme that achieves the desired level of soft error protection according to the demand of each task. The key idea is that for a given protection ratio, compilers can transform a hot loop such that the number of its iterations protected over the total iterations matches the ratio. Compared to full resilience protecting every iteration, RTailor's parameterized soft error resilience significantly reduces the performance overhead of tasks, thereby improving their real-time schedulability. The experimental results highlight that for four representative fault rates, RTailor achieves 15%~average schedulability improvements over the state-of-the-art work that lacks parameterized soft error resilience.

Chinese calligraphy is a unique art form with great artistic value but difficult to master. In this paper, we formulate the calligraphy writing problem as a trajectory optimization problem, and propose an improved virtual brush model for simulating the real writing process. Our approach is inspired by pseudospectral optimal control in that we parameterize the actuator trajectory for each stroke as a Chebyshev polynomial. The proposed dynamic virtual brush model plays a key role in formulating the objective function to be optimized. Our approach shows excellent performance in drawing aesthetically pleasing characters, and does so much more efficiently than previous work, opening up the possibility to achieve real-time closed-loop control.