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ETA-HP: an energy and temperature-aware real-time scheduler for heterogeneous platforms

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Abstract

Modern real-time systems are based on heterogeneous multicore platforms, which help them productively meet the applications’ diverse and high computational requirements. Managing the energy and temperature of these computational platforms has become a topic of inconceivable enthusiasm for researchers and specialists over recent years. This paper presents a heuristic technique, named ETA-HP, for energy and temperature efficient scheduling of a set of real-time periodic tasks on a DVFS empowered heterogeneous multicore system. The proposed strategy operates in four stages, namely Deadline Partitioning, Task-to-Core Allocation, Temperature-Aware Scheduling, and Energy-Aware Scheduling. Our empirical analysis shows that with a variation in system workload from \(50\%\) to \(100\%\), ETA-HP can schedule more tasks (\(2.52\%\) on an average) compared to the state of the art while achieving \(7.29\%\) average energy savings with \(9.59^{\circ }\hbox {C}\) reduction in the average temperature of our considered heterogeneous chip-multiprocessor consisting 4 in-order and 4 out-of-order cores.

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Notes

  1. Based on the execution requirements of individual tasks, we simulate PARSEC application (continuous execution in RoI) accordingly (by specifying the execution span in Gem5 simulator) and obtain the respective execution requirements.

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Acknowledgements

This work is funded by Marie Curie Individual Fellowship (MSCA-IF), EU (Grant Number 898296).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT), Guwahati, India

    Yanshul Sharma & Sanjay Moulik

  2. Department of Computer Science (IDI), Norwegian University of Science and Technology (NTNU), Trondheim, Norway

    Shounak Chakraborty

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  1. Yanshul Sharma
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Correspondence to Sanjay Moulik.

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Sharma, Y., Chakraborty, S. & Moulik, S. ETA-HP: an energy and temperature-aware real-time scheduler for heterogeneous platforms. J Supercomput 78, 1–25 (2022). https://doi.org/10.1007/s11227-021-04257-7

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