In this work, we introduce a power-consumption model for heterogeneous multicore architectures that captures the variability of energy consumption based on processing workload type, in addition to the classical variables considered in the literature, like type and frequency of the CPU. We motivate the approach presenting experimental results gathered on a Odroid-XU3 board equipped with an Arm big.LITTLE CPU, showing that power consumption has a non-negligible dependency on the workload type. We also present a model to define the execution time of the tasks, which depends on both the workload, and the CPU frequency and architecture. We present our modifications to the open-source RTSIM real-time scheduling simulator to extend its CPU power consumption and execution time duration models, integrating results taken from the real platform. The presented work constitutes a useful base for future research in power-aware real-time scheduling on heterogeneous platforms.

Modeling and simulation of power consumption and execution times for real-time tasks on embedded heterogeneous architectures

Alessio Balsini;Luigi Pannocchi;Tommaso Cucinotta
2018-01-01

Abstract

In this work, we introduce a power-consumption model for heterogeneous multicore architectures that captures the variability of energy consumption based on processing workload type, in addition to the classical variables considered in the literature, like type and frequency of the CPU. We motivate the approach presenting experimental results gathered on a Odroid-XU3 board equipped with an Arm big.LITTLE CPU, showing that power consumption has a non-negligible dependency on the workload type. We also present a model to define the execution time of the tasks, which depends on both the workload, and the CPU frequency and architecture. We present our modifications to the open-source RTSIM real-time scheduling simulator to extend its CPU power consumption and execution time duration models, integrating results taken from the real platform. The presented work constitutes a useful base for future research in power-aware real-time scheduling on heterogeneous platforms.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11382/527209
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