Supporting Swap in Real-Time Task Scheduling for Unified Power-Saving in CPU and Memory

Suji Yoon, Heejin Park, Kyungwoon Cho, Hyokyung Bahn

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


As the size of data grows rapidly in modern IoT (Internet-of-Things) and CPS (Cyber-Physical System) applications, the memory power consumption of real-time embedded systems increases dramatically. Unlike general-purpose systems where memory consumes about 10% of the CPU power consumption, modern real-time systems have the memory power of 20-50% of CPU power. This is because the memory size of a real-time system should be large enough to accommodate the entire task set, and thus DRAM refresh operations become a major source of power consumption. In this article, we present a new swap scheme for real-time systems, which aims at reducing memory power consumption. To support swap with real-time constraints, we adopt high-speed NVM storage and co-optimize power-savings in CPU and memory. Unlike traditional real-time task models that only consider the executions in CPU, we define an extended task model that characterizes memory and storage paths of tasks as well, and tightly evaluate the worst-case execution time by formulating the overlapped latency between CPU and memory. By optimizing the CPU supply voltage and the memory swap ratio of given task set, our scheme reduces the energy consumption of real-time systems by 31.1% on average under various workload conditions.

Original languageEnglish
Pages (from-to)3559-3570
Number of pages12
JournalIEEE Access
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Deadline
  • Genetic algorithm
  • High-speed storage
  • NVM
  • Partial swap
  • Power saving
  • Real-time task scheduling
  • Voltage scaling


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