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ReMap: Reliability management of peak-power-aware real-time embedded systems through task replication

Yeganeh-Khaksar, A ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1109/TETC.2020.3018902
  3. Publisher: IEEE Computer Society , 2022
  4. Abstract:
  5. Increasing power densities in future technology nodes is a crucial issue in multicore platforms. As the number of cores increases in them, power budget constraints may prevent powering all cores simultaneously at full performance level. Therefore, chip manufacturers introduce a power budget constraint as Thermal Design Power (TDP) for chips. Meanwhile, multicore platforms are suitable for the implementation of fault-tolerance techniques to achieve high reliability. Task Replication is a well-known technique to tolerate transient faults. However, careless task replication may lead to significant peak power consumption. In this article, we consider the problem of achieving a given reliability target while keeping the total power consumption under the chip TDP for a set of periodic soft real-time tasks. For this purpose, we propose a method for mapping and scheduling periodic soft real-time tasks in multicore embedded systems. The proposed method consists of three parts: (i) Reliability-Aware Lowest Utilization Mapping, (ii) Maximum-Power-Aware EDF Scheduling, and (iii) Reliability-and-Peak-Power-Aware Dynamic-Voltage-Frequency-Scaling. Our experiments show that our proposed method provides up to 38.4 percent (on average by 25 percent) peak power reduction compared to state-of-the-art methods. © 2013 IEEE
  6. Keywords:
  7. Embedded systems ; Multicore platforms ; Reliability ; Task replication ; Thermal design power ; Budget control ; Dynamic frequency scaling ; Electric power utilization ; Embedded systems ; Fault tolerance ; Mapping ; Scheduling ; Voltage scaling ; Dynamic voltage frequency scaling ; Fault tolerance techniques ; Multi-core embedded systems ; Peak power reduction ; Real-time embedded systems ; Reliability management ; State-of-the-art methods ; Total power consumption ; Real time systems
  8. Source: IEEE Transactions on Emerging Topics in Computing ; Volume 10, Issue 1 , 2022 , Pages 312-323 ; 21686750 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/9174780