In modern multi-core Mixed-Criticality (MC) systems, a rise in peak power consumption due to parallel execution of tasks with maximum frequency, specially in the overload situation, may lead to thermal issues, which may affect the reliability and timeliness of MC systems. Therefore, managing peak power consumption has become imperative in multi-core MC systems. In this regard, we propose an online peak power and thermal management heuristic for multi-core MC systems. This heuristic reduces the peak power consumption of the system as much as possible during runtime by exploiting dynamic slack and per-cluster Dynamic Voltage and Frequency Scaling (DVFS). Specifically, our approach examines multiple tasks ahead to determine the most appropriate one for slack assignment, that has the most impact on the system peak power and temperature. However, changing the frequency and selecting a proper task for slack assignment and a proper core for task re-mapping at runtime can be time-consuming and may cause deadline violation which is not admissible for high-criticality tasks. Therefore, we analyze and then optimize our run-time scheduler and evaluate it for various platforms. The proposed approach is experimentally validated on the ODROID-XU3 (DVFS-enabled heterogeneous multi-core platform) with various embedded real-time benchmarks. Results show that our heuristic achieves up to 5.25% reduction in system peak power and 20.33% reduction in maximum temperature compared to an existing method while meeting deadline constraints in different criticality modes.

With the advancement of technology scaling, multi/many-core platforms are getting more attention in embedded systems due to the ever-increasing performance requirements and power efficiency. This feature size scaling, along with architectural innovations, has dramatically exacerbated the rate of manufacturing defects and physical fault-rates. As a result, in addition to providing high parallelism, such hardware platforms have introduced increasing unreliability into the system. Such systems need to be well designed to ensure long-term and application-specific reliability, especially in mixed-criticality systems, where incorrect execution of applications may cause catastrophic consequences. However, the optimal allocation of applications/tasks on multi/many-core platforms is an increasingly complex problem. Therefore, reliability-aware resource management is crucial while ensuring the application-specific Quality-of-Service (QoS) requirements and optimizing other system-level performance goals. This article presents a survey of recent works that focus on reliability-aware resource management in multi-/many-core systems. We first present an overview of reliability in electronic systems, associated fault models and the various system models used in related research. Then, we present recent published articles primarily focusing on aspects such as application-specific reliability optimization, mixed-criticality awareness, and hardware resource heterogeneity. To underscore the techniques’ differences, we classify them based on the design space exploration. In the end, we briefly discuss the upcoming trends and open challenges within the domain of reliability-aware resource management for future research.

Mixed-Criticality (MC) systems have emerged as an effective solution in various industries, where multiple tasks with various real-time and safety requirements (different levels of criticality) are integrated onto a common hardware platform. In these systems, a fault may occur due to different reasons, e.g., hardware defects, software errors or the arrival of unexpected events. In order to tolerate faults in MC systems, the re-execution technique is typically employed, which may lead to overrun of high-criticality tasks (HCTs), which necessitates the drop of low-criticality tasks (LCTs) or degrading their quality. However, frequent drops or relatively long execution times of LCTs (especially mission-critical tasks) are not always desirable and it may impose a negative impact on the performance, or the functionality of MC systems. In this regard, this article proposes a realistic MC task model and develops a design-time task-drop aware schedulability analysis based on the Earliest Deadline First with Virtual Deadline (EDF-VD) algorithm. According to this analysis and the proposed scheduling policy based on the new MC task model, in the high-criticality (HI) mode, when an HCT overruns and the system switches to the HI mode, the number of drops per LCT is prohibited from passing a predefined threshold. In addition, to guarantee the real-time constraints and safety requirements of MC tasks in the presence of faults (assuming transient faults in this article), a corresponding scheduling mechanism has been developed. According to the obtained results from an extensive set of simulations, which have been validated through a realistic avionic application, the proposed method improves the acceptance ratio by up to 43.9% compared to state-of-the-art.

Mixed-criticality (MC) systems have recently been devised to address the requirements of real-time systems in industrial applications, where the system runs tasks with different criticality levels on a single platform. In some workloads, a high-critically task might overrun and overload the system, or a fault can occur during the execution. However, these systems must be fault tolerant and guarantee the correct execution of all high-criticality (HC) tasks by their deadlines to avoid catastrophic consequences, in any situation. Furthermore, in these MC systems, the peak-power consumption of the system may increase, especially in an overload situation and exceed the processor thermal design power (TDP) constraint. This may cause generating heat beyond the cooling capacity, resulting the system stop to avoid excessive heat and halting the processor. In this article, we propose a technique for dependent dual-criticality tasks in fault-tolerant multicore MC systems to manage peak-power consumption and temperature. The technique develops a tree of possible task mapping and scheduling at design-time to cover all possible scenarios and reduce the low-criticality task drop rate in the HC mode. At the runtime, the system exploits the tree to select a proper schedule according to fault occurrences and criticality mode changes. Experimental results show that the average task schedulability is 74.14% on average for the proposed method, while the peak-power consumption and maximum temperature are improved by 16.65% and 14.9 °C on average, respectively, compared to a recent work. In addition, for a real-life application, our method reduces the peak power and maximum temperature by up to 20.06% and 5 °C, respectively, compared to a state-of-the-art approach.

In this work, we address peak power and maximum temperature in multi-core Mixed-Criticality (MC) systems. In these systems, a rise in peak power consumption may generate more heat beyond the cooling capacity. Additionally, the reliability and timeliness of MC systems may be affected due to excessive temperature. Therefore, managing peak power consumption has become imperative in multi-core MC systems. In this regard, we propose an online peak power management heuristic for multi-core MC systems. This heuristic reduces the peak power consumption of the system as much as possible during runtime by exploiting dynamic slack and Dynamic Voltage and Frequency Scaling (DVFS). Specifically, our approach examines multiple tasks ahead to determine the most appropriate one for slack assignment instead of just one task as in the literature. The selection is based on the impact of the tasks on peak power and temperature of the system. The DVFS is then applied to that task to reduce the system peak power and maximum temperature. Further, a re-mapping technique is proposed to further improve the results. Our experimental results show that our heuristic achieves up to 18.2% reduction in system peak power consumption and 8.1% reduction in maximum temperature compared to an existing method. The inherent energy consumption is also reduced by up to 50%.

In Mixed-Criticality (MC) systems, there are often multiple Worst-Case Execution Times (WCETs) for the same task, corresponding to system operation mode. Determining the appropriate WCETs for lower criticality modes is non-trivial; while on the one hand, a low WCET for a mode can improve the processor utilization in that mode, on the other hand, using a larger WCET ensures that the mode switches are minimized, thereby maximizing the quality-of-service for all tasks, albeit at the cost of processor utilization. Although there are many studies to determine WCET in the highest criticality mode, no analytical solutions are proposed to determine WCETs in other lower criticality modes. In this regard, we propose a scheme to determine WCETs by Chebyshev theorem to make a trade-off between the number of scheduled tasks at design-time and the number of dropped low-criticality tasks at runtime as a result of frequent mode switches. Our experimental results show that our scheme improves the utilization of state-of-the-art MC systems by up to 85.29%, while maintaining 9.11% mode switching probability in the worst-case scenario.