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Thermal-aware resource management for embedded real-time systems
- Thermal-aware resource management for embedded real-time systems
- Lee, Youngmoon; Chwa, Hoon Sung; Shin, Kang G.; Wang, Shige
- DGIST Authors
- Chwa, Hoon Sung
- Issue Date
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(11), 2857-2868
- Article Type
- Dynamic ambient temperature; embedded real-time systems; task-level power dissipation; thermal-aware resource management; Distributed computer systems; Dynamics; Electric losses; Embedded systems; Interactive computer systems; Natural resources management; Resource allocation; System-on-chip; Temperature; Thermal management (electronics); Adaptive parameters; Automotive microcontrollers; Embedded real time systems; Resource management; Resource management framework; Resource utilizations; Task levels; Temperature variation; Real time systems
- With an increasing demand for complex and powerful system-on-chips, modern real-time automotive systems face significant challenges in managing on-chip-temperature. We demonstrate, via real experiments, the importance of accounting for dynamic ambient temperature and task-level power dissipation in resource management so as to meet both thermal and timing constraints. To address this problem, we propose RT-TRM, a real-time thermal-aware resource management framework. We first introduce a task-level dynamic power model that can capture different power dissipations with a simple task-level parameter called the activity factor. We then develop two new mechanisms, adaptive parameter assignment and online idle-time scheduling. The former adjusts voltage/frequency levels and task periods according to the varying ambient temperature while preserving feasibility. The latter generates a schedule by allocating idle times efficiently without missing any task/job deadline. By tightly integrating the solutions of these two mechanisms, we can guarantee both thermal and timing constraints in the presence of dynamic ambient temperature variations. We have implemented RT-TRM on an automotive microcontroller to demonstrate its effectiveness, achieving better resource utilization by 18.2% over other runtime approaches while meeting both thermal and timing constraints. © 2018 IEEE.
- Institute of Electrical and Electronics Engineers Inc.
- Related Researcher
Chwa, Hoon Sung
Real-Time Computing Lab
Real-Time Systems; Real-Time AI Services; Cyber-Physical Systems; Mobile Systems
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- Department of Information and Communication EngineeringReal-Time Computing Lab1. Journal Articles
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