Abstract
Multicore platforms offer great potential for task-level redundancy to achieve a degree of fault-tolerance/reliability in embedded systems by exploiting the idle cores. However, due to the Thermal Design Power (TDP) constraint, it may not be possible to simultaneously power-on all cores in a multicore chip at the full-throttle (e.g., in ARM's big.LITTLE architecture). Since TDP is the maximum sustainable power that a chip can dissipate safely (as per the specifications given by a chip vendor), violating TDP triggers a performance throttling mechanism (e.g., by lowering the operating voltage and frequency, or by power-gating with task migration) to avoid possible overheating problems. This can significantly affect the timeliness of the system, and hence, represents a serious challenge in using (off-the-shelf) multicore platforms in real-time embedded systems when exploiting it for full-scale reliability. That means only a few tasks can be afforded to run in a fully reliable mode under a given TDP constraint. In this article, at first, we study the power consumption of task-level redundancy running on multicore platforms. Then, to tackle the peak power problem, we propose a novel primary-backup scheme for power-aware scheduling of real-time tasks on core pairs in multicore systems. The proposed scheme aims at removing overlaps of peak power of concurrently executing tasks to keep the power consumption below the chip-level TDP constraint. This would facilitate higher reliability levels within a given power budget. To do this, considering the tasks' power profiles, we propose a task partitioning method along with maximum-peak-power-first (MPPF) and maximum-peak-power-last (MPPL) policies to schedule original and redundant copies of tasks, respectively. Our experiments show that our technique provides up to 50% (on average by 29.5%) peak power reduction compared to state-of-the-art schemes, while providing the same reliability level.
Highlights
Technology scaling allows integrating multiple cores onto a single chip for advanced embedded systems [1]–[4]
Our experiments show that our technique provides up to 50% peak power reduction compared to the other schemes in the worst-case fault condition
Since solving the above integer linear programing (ILP) problem and finding a schedule for a multicore system to optimally minimize peak power consumption is an NP-hard problem [1], [13], [47], we present a heuristic to provide a solution for peak power reduction
Summary
Technology scaling allows integrating multiple cores onto a single chip for advanced embedded systems [1]–[4]. Our technique schedules real-time tasks on core pairs in a multicore system without violating the tasks’ timing constraints This method aims at removing overlaps of peak power of concurrently executing tasks to keep the peak power consumption below the chip TDP. Haque et al [23] propose exploiting the earliest-deadlinefirst (EDF) and earliest-deadline-late (EDL) policies to schedule the original and redundant tasks, respectively These methods effectively reduce average power consumption by dropping unnecessary redundant executions when no fault occurs. B. OUR NOVEL CONTRIBUTIONS AND CONCEPT OVERVIEW In this article, we propose a peak-power-aware primarybackup technique that enables task-level redundancy to achieve fault tolerance in multicore embedded systems under timing and TDP constraints. A scheduling algorithm for the primary-backup technique to enable task-level redundancy on multicore systems This algorithm reduces peak power consumption through partitioning the tasks into parts without violating any real-time constraint. MODELS AND ASSUMPTIONS we present the models and preliminaries of the paper
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