In Mixed-Criticality (MC) systems, due to encountering multiple Worst-Case Execution Times (WCETs) for each task corresponding to the system operation modes, estimating appropriate WCETs for tasks in ...lower-criticality (LO) modes is essential to improve the system's timing behavior. While numerous studies focus on determining WCET in the high-criticality mode, determining the appropriate WCET in the LO mode poses significant challenges and has been addressed in a few research works due to its inherent complexity. This article introduces ESOMICS, a novel scheme, to obtain appropriate WCET for LO modes, in which we propose an ML-based approach for WCET estimation based on the application's source code analysis and the model training using a comprehensive data set. The experimental results show a significant improvement in utilization by up to 23.3% compared to state-of-the-art works, while mode switching probability is bounded by 7.19%, in the worst-case scenario.
The massive parallelism provided by Graphics Processing Units (GPUs) to accelerate compute-intensive tasks makes it preferable for Real-Time Systems such as autonomous vehicles. Such systems require ...the execution of heavy Machine Learning (ML) and Computer Vision applications because of the computing power of GPUs. However, such systems need a guarantee of timing predictability. It means the Worst-Case Execution Time (WCET) of the application is estimated tightly and safely to schedule each application before its deadline to avoid catastrophic consequences. As more applications use GPUs, running many applications simultaneously on the same GPU becomes necessary. To provide predictable performance while the application is running in parallel, it must be WCET-aware, which GPUs do not fully support in a multitasking environment. Nvidia recently added a feature called the Multi-Process Service. It allows the different applications to run simultaneously in the same CUDA context by partitioning the compute resources of the GPU. Using this feature, we can measure the interference from co-running GPU applications to estimate WCET. In this paper, we propose a novel technique to estimate the WCET of the GPU kernel using an ML approach. Our approach is based on the application's source, and the model is trained based on the large data set. The approach is flexible and can be applied to different GPU-sharing mechanisms. We allow the victim and enemy kernel of the GPU to execute in parallel to get the maximum interference from the enemy to estimate the WCET of the victim kernel. Enemy kernels are chosen to cause a higher slowdown by acquiring the resources of the victim kernel. We compare our implementation with state-of-the-art approaches to show its effectiveness. Our ML approach reduces the time by 99% in most cases because inferences take only seconds to predict WCET, and the resource consumption required to estimate WCET compared to traditional approaches is minimal because we don't need to execute the application on GPU for hours. Although our approach does not offer safety guarantees because of its empirical nature, we observed that predicted WCETs are always higher than any observed execution times for all benchmarks, and the maximum overestimation factor observed is 11x.
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.
In Mixed-Criticality (MC) systems, multiple functions with different levels of criticality are integrated into a common platform in order to meet the intended space, cost, and timing requirements in ...all criticality levels. To guarantee the correct, and on-time execution of higher criticality tasks in emergency modes, various design-time scheduling policies have been recently presented. These techniques are mostly pessimistic, as the occurrence of worst-case scenario at run-time is a rare event. Nevertheless, they lead to an under-utilized system due to frequent drops of Low-Criticality (LC) tasks, and creation of unused slack times due to the quick execution of high-criticality tasks. Accordingly, this paper proposes a novel optimistic scheme, that introduces a learning-based drop-aware task scheduling mechanism, which carefully monitors the alterations in the behaviour of the MC system at run-time, to exploit the generated dynamic slacks for reducing the LC tasks penalty and preventing frequent drops of LC tasks in the future. Based on an extensive set of experiments, our observations have shown that the proposed approach exploits accumulated dynamic slack generated at run-time, by 9.84% more on average compared to existing works, and is able to reduce the deadline miss rate by up to 51.78%, and 33.27% on average, compared to state-of-the-art works.
Mixed-Criticality Systems (MCSs) include tasks with multiple levels of criticality and different modes of operation. These systems bring benefits such as energy and resource saving while ensuring ...safe operation. However, management of available resources in order to achieve high utilization, low power consumption, and required reliability level is challenging in MCSs. In many cases, there is a trade-off between these goals. For instance, although using fault-tolerance techniques, such as replication, leads to improving the timing reliability, it increases power consumption and can threaten life-time reliability. In this work, we introduce an approach named Life-time Peak Power management in Mixed-Criticality systems (LPP-MC) to guarantee reliability, along with peak power reduction. This approach maps the tasks using a novel metric called Reliability-Power Metric (RPM). The LPP-MC approach uses this metric to balance the power consumption of different processor cores and to improve the life-time of a chip. Moreover, to guarantee the timing reliability of MCSs, a fault-tolerance technique, called task re-execution, is utilized in this approach. We evaluate the proposed approach by a real avionics task set, and various synthetic task sets. The experimental results show that the proposed approach mitigates the aging rate and reduces peak power by up to 20.6% and 17.6%, respectively, 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.
Successful formation of a three-dimensional (3D) network of incorporated conductive fillers in a polymer matrix leads to achieve an electrically conductive nanocomposite at low filler loading levels. ...In this work, one- to three-layer edge and basal-functionalized graphene oxide (GO) nanosheets were synthesized via a novel method. Raman spectroscopy was employed to investigate the localization of oxygen-containing groups through the GO nanosheets. Afterward, the synthesized GO nanosheets were dispersed in an aqueous epoxy suspension to produce electrically conductive polymer nanocomposites. The formation of the interconnected 3D network structure of GO nanosheets through the epoxy matrix was studied by employing rheological approaches and imaging techniques. We postulated that oxygen-containing groups’ localization can effectively impact the polymer–GO nanosheet interactions, which, in turn, affect the 3D network formation of the nanosheets through the polymeric medium. After in situ thermal reduction of polymer nanocomposites at 225 °C, electrical conductivity measurements revealed that nanocomposites containing basal-functionalized graphene nanosheets featured higher electrical conductivity values compared to those for samples containing edge-functionalized graphene nanosheets. Hence, these results shed light on the importance of functional groups localization that can dictate the final properties.
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Performance of graphene nanoplatelets depends on its dispersion quality and level of homogeneity, to this end investigating graphene dispersing approaches from different point of view ...will be highly applicable. In this study, we compared the effects of different surfactants—sodium dodecylsulfate (SDS), sodium dodecylbenzen sulfonate (SDBS), cetyltrimethylammonium bromide (CTAB), and nonylphenolethoxylate (NPE)—in their capacity to produce stable aqueous media containing homogeneously dispersed graphene nanoplatelets (GNPs). To compare the surfactants’ ability to produce optimal dispersion of graphene, we relied on optical characterization, i.e. UV–vis spectroscopy, optical microscopy, and turbidimetry. CTAB-containing GNP dispersion showed the highest stability and lowest graphene flake size. The character of the different surfactants led us to investigate the stability mechanisms in the aqueous dispersions. Zeta potential was measured to determine the effective surface charge of GNPs in each aqueous medium. The accordance between experimental results and theories in this regard increases the reliability of results. Hence, we evaluated the results through DLVO theory to rank the performance of surfactants more evidently. Based on zeta potential values and the height of the energy barrier in interaction energy graph, CTAB had the best performance in terms of stability and preventing GNP aggregate formation. Molecular dynamics simulation was conducted to have better understanding of interactions between different surfactants and graphene nanosheets. The performance of the surfactants was ranked in order of CTAB, SDBS, SDS, NPE. Using UV–vis spectroscopy, particle size analysis, TEM and SEM imagery, we determined that the optimal concentration of CTAB is 400 ppm.
In Mixed-Criticality (MC) systems, due to encoun-tering multiple Worst-Case Execution Times (WCETs) for each task corresponding to the system operation modes, estimating appropriate WCETs for tasks ...in lower-criticality (LO) modes is essential to improve the system's timing behavior. While numerous studies focus on determining WCET in the high-criticality mode, determining the appropriate WCET in the LO mode poses significant challenges and has been addressed in a few research works due to its inherent complexity. This article introduces a novel scheme to obtain appropriate WCET for LO modes. We propose an ML-based approach for WCET estimation based on the application's source code analysis and the model training using a comprehensive data set. The experimental results show a significant improvement in utilization by up to 23.3 % for the ML-based approach, while mode switching probability is bounded by 7.19 % in the worst-case scenario.
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