TY - JOUR
T1 - Peak-Power-Aware Primary-Backup Technique for Efficient Fault-Tolerance in Multicore Embedded Systems
AU - Ansari, Mohsen
AU - Salehi, Mohammad
AU - Safari, Sepideh
AU - Ejlali, Alireza
AU - Shafique, Muhammad
N1 - Funding Information:
This work was supported in part by the German Research Foundation (DFG) as part of the GetSURE Project in the scope of priority program Dependable Embedded Systems (SPP 1500).
Publisher Copyright:
© 2013 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - efficiency
KW - embedded systems
KW - fault-tolerance
KW - multicore platforms
KW - Peak power
KW - primary-backup technique
KW - real-time
KW - thermal design power
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U2 - 10.1109/ACCESS.2020.3013721
DO - 10.1109/ACCESS.2020.3013721
M3 - Article
AN - SCOPUS:85089954538
SN - 2169-3536
VL - 8
SP - 142843
EP - 142857
JO - IEEE Access
JF - IEEE Access
M1 - 9154674
ER -