Towards Compositional Mixed-Criticality Real-Time Scheduling in Open Systems

Although many cyber-physical systems are both mixed-criticality system and compositional system, there are little work on intersection of mixed-criticality system and compositional system. We propose novel concepts for task-level criticality mode and reconsider temporal isolation in terms of compositional mixed-criticality scheduling

MC-ADAPT: Adaptive Task Dropping in Mixed-Criticality Scheduling

Recent embedded systems are becoming integrated systems with components of different criticality. To tackle this, mixed-criticality systems aim to provide different levels of timing assurance to components of different criticality levels while achieving efficient resource utilization. Many approaches have been proposed to execute more lower-criticality tasks without affecting the timeliness of higher-criticality tasks. Those previous approaches however have at least one of the two limitations; i) they penalize all lower-criticality tasks at once upon a certain situation, or ii) they make the decision how to penalize lowercriticality tasks at design time. As a consequence, they underutilize resources by imposing an excessive penalty on lowcriticality tasks. Unlike those existing studies, we present a novel framework, called MC-ADAPT, that aims to minimally penalize lower-criticality tasks by fully reflecting the dynamically changing system behavior into adaptive decision making. Towards this, we propose a new scheduling algorithm and develop its runtime schedulability analysis capable of capturing the dynamic system state. Our proposed algorithm adaptively determines which task to drop based on the runtime analysis. To determine the quality of task dropping solution, we propose the speedup factor for task dropping while the conventional use of the speedup factor only evaluates MC scheduling algorithms in terms of the worst-case schedulability. We apply the speedup factor for a newly-defined task dropping problem that evaluates task dropping solution under different runtime scheduling scenarios. We derive that MC-ADAPT has a speedup factor of 1.619 for task drop. This implies that MC-ADAPT can behave the same as the optimal scheduling algorithm with optimal task dropping strategy does under any runtime scenario if the system is sped up by a factor of 1.619

Tight necessary feasibility analysis for recurring real-time tasks on a multiprocessor

One of the important design issues for time-critical embedded systems is to derive necessary conditions that meet all job deadlines invoked by a set of recurring real-time tasks under a computing resource (called feasibility). To this end, existing studies focused on how to derive a tight lower-bound of execution requirement (i.e., demand) of a target set of real-time tasks. In this paper, we address the following question regarding the supply provided by a multiprocessor resource: is it possible for a real-time task set to always utilize all the provided supply? We develop a systematic approach that i) calculates the amount of supply proven unusable, ii) finds a partial schedule that yields a necessary condition to minimize the amount of unusable supply, and iii) uses the partial schedule to further reclaim unusable supply. While the systematic approach can be applied to most (if not all) recurring real-time task models, we show two examples how the approach can yield tight necessary feasibility conditions for the sequential task model and the gang scheduling model. We demonstrate the proposed approach finds a number of additional infeasible task sets which have not been proven infeasible by any existing studies for the task models. © 2022 Elsevier B.V.FALS

Adaptive brake system software platform for self-driving cars

The brake system of a self-driving car is one of the most important systems for ensuring safety. A typical brake system performs several computational tasks, including perception, high-level brake control, and low-level electromechanical control tasks. The status-quo design for scheduling such brake-related tasks is based on the static approach where all parameters for those tasks are fixed when designing the brake system. Such a static approach has the following limitations in terms safety and resource efficiency: i) It cannot adap-tively respond to dynamic environments, such as varying road friction coefficients and the time to collision. ii) The brake operation time constitutes only a small portion of total driving time. Hence, to address this issue, we propose a new adaptive brake system software platform that enables adaptive parameter assignment and dynamic online scheduling to cope with dynamic environments. We implemented and integrated the proposed adaptive parameter assignment and scheduling platform into an AUTOSAR-based brake system, an open and standardized automotive software architecture. Thus, we could significantly improve safety and reliability by shortening the braking distance. © ICROS 2021.1

Infeasibility Test for Fixed-Priority Scheduling on Multiprocessor Platforms

Fixed-Priority Scheduling (FPS), due to its simplicity to implement, has been one of the most popular scheduling algorithms for real-time embedded systems equipped with multiprocessor platforms. While there have been many studies that find sufficient conditions for a given task set to be feasible (schedulable) by FPS with a proper priority assignment, the other direction (i.e., finding infeasible task sets) has not been studied. In this letter, we address a necessary feasibility condition that judges a given task set to be infeasible under FPS with every priority assignment on multiprocessor platforms. To this end, we derive useful properties for the condition, and develop the first infeasibility test for FPS on multiprocessor platforms. Via simulations, we show that the proposed infeasibility test discovers a number of FPS-infeasible task sets which are not proven FPSinfeasible by any existing studies. IEEE1

Necessary Feasibility Analysis for Mixed-Criticality Real-Time Embedded Systems

As multiple software components with different safety-criticality levels are integrated on a shared computing platform, a real-time embedded system becomes a mixed-criticality (MC) system, which should provide timing guarantees at all different levels of assurance to software components with different criticality levels. In the real-time systems community, the concept of an MC system is regarded as a promising, emerging solution to solve an inherent challenge of real-time systems: pessimistic reservation of computing resources, which yields a low resource-utilization for the sake of guaranteeing timing requirements. Since a timing guarantee should be provided before a real-time system starts to operate, its feasibility has been extensively studied for single-criticality systems; however, the same cannot be said for MC systems. In this article, we develop necessary feasibility tests for MC real-time embedded systems, which is the first study that yields non-trivial results for MC necessary feasibility on both uniprocessor and multiprocessor platforms. To this end, we investigate characteristics of MC necessary feasibility conditions, and identify new challenges posed by the characteristics. By addressing those challenges, we develop two collective necessary feasibility tests and their simplified versions, which are able to exploit a tradeoff between capability in finding infeasible task sets and time-complexity. The simulation results demonstrate that the proposed tests find a number of additional infeasible task sets for both uniprocessor and multiprocessor platforms, which have been proven neither feasible nor infeasible by any existing studies. © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.FALS

Dynamic Chip Clustering and Task Allocation for Real-time Flash

The goal of this paper is to provide worst-case timing guarantees for real-time I/O requests, while fully utilizing the potential bandwidth for non real-time I/O requests in NAND flash storage systems. We identify a trade-off between flash chip sharing and I/O workload isolation in terms of timing guarantees and bandwidth. By taking such a trade-off into account, we propose a new real-time I/O scheduling framework that enables dynamic isolation between real-time I/O requests to meet all timing constraints and co-scheduling of real-time and non realtime I/O requests to provide high bandwidth utilization. Our in-depth evaluation results show that the proposed approach outperforms existing isolation approaches significantly in terms of both schedulability and bandwidth. © 2021 IEEE

Thermal-Aware Scheduling for Integrated CPUs-GPU Platforms

As modern embedded systems like cars need high-power integrated CPUs-GPU SoCs for various real-time applications such as lane or pedestrian detection, they face greater thermal problems than before, which may, in turn, incur higher failure rate and cooling cost. We demonstrate, via experimentation on a representative CPUs-GPU platform, the importance of accounting for two distinct thermal characteristics-the platform's temperature imbalance and different power dissipations of different tasks-in real-time scheduling to avoid any burst of power dissipations while guaranteeing all timing constraints. To achieve this goal, we propose a new Real-Time Thermal-Aware Scheduling (RT-TAS) framework. We first capture different CPU cores' temperatures caused by different GPU power dissipations (i.e., CPUs-GPU thermal coupling) with core-specific thermal coupling coefficients. We then develop thermally-balanced task-to-core assignment and CPUs-GPU co-scheduling. The former addresses the platform's temperature imbalance by efficiently distributing the thermal load across cores while preserving scheduling feasibility. Building on the thermally-balanced task assignment, the latter cooperatively schedules CPU and GPU computations to avoid simultaneous peak power dissipations on both CPUs and GPU, thus mitigating excessive temperature rises while meeting task deadlines. We have implemented and evaluated RT-TAS on an automotive embedded platform to demonstrate its effectiveness in reducing the maximum temperature by 6−12.2◦C over existing approaches without violating any task deadline. © 2019 Association for Computing Machinery.FALS

Thermal-Aware Resource Management for Embedded Real-Time Systems

With an increasing demand for complex and powerful system-on-chips, modern real-time automotive systems face significant challenges in managing on-chip-temperature. We demonstrate, via real experiments, the importance of accounting for dynamic ambient temperature and task-level power dissipation in resource management so as to meet both thermal and timing constraints. To address this problem, we propose RT-TRM, a real-time thermal-aware resource management framework. We first introduce a task-level dynamic power model that can capture different power dissipations with a simple task-level parameter called the activity factor. We then develop two new mechanisms, adaptive parameter assignment and online idle-time scheduling. The former adjusts voltage/frequency levels and task periods according to the varying ambient temperature while preserving feasibility. The latter generates a schedule by allocating idle times efficiently without missing any task/job deadline. By tightly integrating the solutions of these two mechanisms, we can guarantee both thermal and timing constraints in the presence of dynamic ambient temperature variations. We have implemented RT-TRM on an automotive microcontroller to demonstrate its effectiveness, achieving better resource utilization by 18.2% over other runtime approaches while meeting both thermal and timing constraints. © 2018 IEEE.1