Evolution-Based Real-Time Job Scheduling for Co-Optimizing Processor and Memory Power Savings

Hyokyung Bahn, Kyungwoon Cho

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


With the recent advances in battery-based mobile computing technologies, power-saving techniques in real-time embedded devices are becoming increasingly important. This paper presents a novel job scheduling policy for real-time systems, which aims at minimizing the power consumption of processor and memory without missing the deadline constraints of real-time jobs. To do so, we formulate the power saving techniques of processor voltage/frequency scaling and memory job placement as a unified measure, and show that it is a complex search problem that has the exponential time complexity. Thus, an efficient heuristic based on evolutionary computation is performed to cut down the huge searching space and find a reasonable schedule within the feasible time budget. To evaluate the proposed scheduling policy, we conduct experiments under various workload conditions. Our experimental results show that the proposed policy significantly reduces the energy consumption of real-time systems. Specifically, the average reduction in the energy consumption is 41.7% without deadline misses.

Original languageEnglish
Article number9169623
Pages (from-to)152805-152819
Number of pages15
JournalIEEE Access
StatePublished - 2020

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIP) under Grant 2019R1A2C1009275, and in part by the ICT Research and Development Program of MSIP/IITP (developing system software technologies for emerging new memory that adaptively learn workload characteristics) under Grant 2019-0-00074.

Publisher Copyright:
© 2013 IEEE.


  • Real-time job scheduling
  • deadline
  • dynamic voltage/frequency scaling
  • evolutionary computation
  • genetic algorithm
  • power saving


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