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On the Scheduling of Energy-Aware Fault-Tolerant Mixed-Criticality Multicore Systems with Service Guarantee Exploration

机译:基于服务保证探索的能量感知容错混合临界多核系统调度

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Advancement of Cyber-Physical Systems has attracted attention to Mixed-Criticality Systems (MCSs), both in research and in industrial designs. As multicore platforms are becoming the dominant trend in MCSs, joint energy and reliability management is a crucial issue. In addition, providing guaranteed service level for low-criticality tasks in critical mode is of great importance. To address these problems, we propose "LETR-MC"scheme that simultaneously supports certification, energy management, fault-tolerance, and guaranteed service level in mixed-criticality multicore systems. In this paper, we exploit task-replication to not only satisfy reliability requirements, but also to improve the QoS of low-criticality tasks in overrun situation. Our proposed LETR-MC scheme determines the number of replicas, and reduces the execution time overlap between the primary tasks and replicas. Moreover, instead of ignoring low-criticality tasks or selectively executing them without any guaranteed service level in overrun mode, it mathematically explores the minimum achievable service guarantee for each low-criticality task in different execution modes, i. e., normal, fault-occurrence, overrun and critical operation modes. We develop novel unified demand bound functions (DBF), along with a DVFS method based on the proposed DBF analysis. Our experimental results show that LETR-MC provides up to 59 percent (24 percent on average) energy saving, and significantly improves the service levels of low-criticality tasks compared to the state-of-the-art schemes.
机译:在研究和工业设计中,网络物理系统的发展已引起人们对混合临界系统(MCS)的关注。随着多核平台成为MCS的主流趋势,联合能源和可靠性管理已成为关键问题。另外,为关键模式下的低关键任务提供有保证的服务水平也非常重要。为了解决这些问题,我们提出了“ LETR-MC”方案,该方案同时支持混合关键多核系统中的认证,能量管理,容错和保证的服务水平。在本文中,我们利用任务复制不仅满足可靠性要求,而且在超载情况下提高了低临界任务的QoS。我们提出的LETR-MC方案确定了副本的数量,并减少了主要任务和副本之间的执行时间重叠。而且,与其在超载模式下忽略低关键性任务或有选择地执行它们而没有任何保证的服务级别,它在数学上探索了在不同执行模式下为每个低关键性任务实现的最小服务保证。例如,正常,故障发生,超限和关键操作模式。我们基于提出的DBF分析,开发了新颖的统​​一需求约束函数(DBF),以及DVFS方法。我们的实验结果表明,与最新方案相比,LETR-MC可以节省多达59%(平均24%)的能源,并显着提高了低临界任务的服务水平。

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