Multicore platforms are becoming the dominant trend in mixed-criticality systems (MCSs). Multicores provide great opportunities to realize task-level redundancy for reliability enhancement. However, they may experience limited utility in battery-powered mixed-criticality embedded systems. Hence, joint energy and reliability management is a crucial issue in designing MCSs. In this article, we propose the low energy standby-sparing mechanism in mixed-criticality system (LESS-MICS) scheme, which uses the inherent redundancy of multicores to apply the standby-sparing technique for fault-tolerance. Also, by using the inherent redundancy, the LESS-MICS scheme proposes the Parallelism and Reduction policy that can be applied to any graph traverse algorithm to enhance the schedulability of graph-based mixed-criticality tasks, as well as joint energy and reliability management, and guarantying an acceptable service level for low-criticality tasks in overrun mode. To achieve further energy reduction, we minimize energy through convex optimization, and also propose energy management heuristics which use dynamic voltage and frequency scaling and dynamic power management. We evaluated our scheme under various system configurations. Experiments show that our scheme provides, on average, 24.2% energy reduction compared to state-of-the-art techniques while preserving an acceptable QoS level.

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.