Exploiting primary/backup mechanism for energy efficiency in dependable real-time systems

Primary/Backup has been well studied as an effective fault-tolerance technique. In this paper, with the objectives of tolerating a single permanent fault and maintaining system reliability with respect to transient faults, we study dynamic-priority based energy-efficient fault-tolerance scheduling algorithms for periodic real-time tasks running on multiprocessor systems by exploiting the primary/backup technique while considering the negative effects of the widely deployed Dynamic Voltage and Frequency Scaling (DVFS) on transient faults. Specifically, by separating primary and backup tasks on their dedicated processors, we first devise two schemes based on the idea of Standby-Sparing (SS) : For Paired-SS , processors are organized as groups of two (i.e., pairs) and the existing SS scheme is applied within each pair of processors after partitioning tasks to the pairs. In Generalized-SS , processors are divided into two groups (of potentially different sizes), which are denoted as primary and secondary processor groups, respectively. The main (backup) tasks are scheduled on the primary (secondary) processor group under the partitioned-EDF ( partitioned-EDL ) with DVFS (DPM) to save energy. Moreover, we propose schemes that allocate primary and backup tasks in a mixed manner to better utilize system slack on all processors for more energy savings. On each processor, the Preference-Oriented Earliest Deadline (POED) scheduler is adopted to run primary tasks at scaled frequencies as soon as possible (ASAP) and backup tasks at the maximum frequency as late as possible (ALAP) to save energy. Our empirical evaluations show that, for systems with a given number of processors, there normally exists a configuration for Generalized-SS with different number of processors in primary and backup groups, which leads to better energy savings when compared to that of the Paired-SS scheme. Moreover, the POED-based schemes normally have more stable performance and can achieve better energy savings. © 2017 Elsevier B.V. All rights reserved.