Supporting Swap in Real-Time Task Scheduling for Unified Power-Saving in CPU and Memory
- 주제(키워드) Task analysis , Real-time systems , Voltage , Memory management , Processor scheduling , Power demand , Nonvolatile memory , Real-time task scheduling , partial swap , genetic algorithm , power saving , voltage scaling , deadline , high-speed storage , NVM
- 주제(기타) Computer Science, Information Systems
- 주제(기타) Engineering, Electrical & Electronic
- 주제(기타) Telecommunications
- 설명문(일반) [Yoon, Suji; Park, Heejin; Cho, Kyungwoon; Bahn, Hyokyung] Ewha Womans Univ, Dept Comp Engn, Seoul 120750, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- OA유형 gold
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2022
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000191066
- 본문언어 영어
- Published As https://doi.org/10.1109/ACCESS.2021.3140166
초록/요약
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.
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