Thermal-aware Resource Management for Modern Computing Platforms in the Next Generation of Aircraft

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Thermal-Aware Schedulability Analysis for Fixed-Priority Non-preemptive Real-Time Systems

Publication . Perez Rodriguez, Javier; Meumeu Yomsi, Patrick

Technology advances in microprocessor design have resulted in high device density and performance during the last decades. More components are fabricated on the chip die and millions, if not billions, of instructions can now be executed within microseconds. A consequence of this advancement is heat dissipation by the microprocessors. In this context, elevated on-chip temperature issues have become an important subject for the design of future generations of microprocessors, especially in avionics and automotive industries. In this paper, we address the scheduling problem of non-preemptive periodic tasks on a single processor platform under thermal-aware design. We assume that the tasks are scheduled by following any Fixed-Task-Priority (FTP) scheduler (e.g., Rate Monotonic (RM) or Deadline Monotonic (DM)) and we propose a unique framework wherein we capture both the temporal and thermal behavior of the system. Then, we present two new thermal-aware scheduling strategies, referred to as NP-HBC and NP-CBH, to keep the system temperature within specified parameters and we derive their respective schedulability analysis. Finally, we evaluate the performance of the proposed theoretical results through intensive simulations.

2020Conference object

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Work-in-Progress: Towards a fine-grain thermal model for uniform multi-core processors

Publication . Pérez Rodríguez, Javier; Meumeu Yomsi, Patrick

On-chip power dissipation is recognized as one of the primary limiters, if not a show stopper, of performance for high-end safety-critical uniform multi-core processors. This paper proposes an efficient and simple thermal model for such a platform to be coupled with the large variety of schedulers designed to control the processor activity and the triggering of the cooling mechanism with as little impact on performance as possible.

2020Conference object

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