Global Reliability-Aware Power Management for Multiprocessor Real-Time Systems

Recently, the negative effect of the popular power management technique Dynamic Voltage and Frequency Scaling (DVFS) on the system reliability has been identified. As a result, various reliability-aware power management (RAPM) schemes have been studied for uniprocessor real-time systems. In this paper, we investigate global scheduling-based RAPM (G-RAPM) schemes for a set of frame-based real-time tasks running on a homogeneous multiprocessor system. An important dimension of the problem is how to select the appropriate subset of tasks for energy and reliability management (i.e., schedule a recovery for each selected task and scale down their executions). We show that making this decision optimally (i.e., the static G-RAPM problem) is NP-hard. Then we propose two efficient G-RAPM heuristics, which rely on local and global task selections, respectively. Moreover, to reclaim dynamic slack generated at runtime, we extend the slack-sharing based global dynamic power management scheme to the reliability-aware settings. The proposed schemes are evaluated through extensive simulations. The results show that our static G-RAPM heuristics can preserve system reliability while achieving significant energy savings (within 3% of an upper bound for most cases). Moreover, G-RAPM with global task selection provides better opportunities for dynamic slack reclamation and up to 15% more energy savings can be obtained at runtime compared to that of local task selection.