Open Access

ARTICLE

Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

Hamayun Khan^1,*, Irfan Ud din², Arshad Ali³, Sami Alshmrany³

1 Department of Electrical Engineering, Faculty of Engineering & Technology Superior University Lahore, 54000, Pakistan
2 Department of Computer Science, Faculty of Computer Science & IT Superior University Lahore, 54000, Pakistan
3 Faculty of Computer and Information Systems, Islamic University of Madinah, Al Madinah Al Munawarah, 42351, Saudi Arabia

* Corresponding Author: Hamayun Khan. Email:

Computers, Materials & Continua 2023, 74(1), 965-981. https://doi.org/10.32604/cmc.2023.031223

Received 12 April 2022; Accepted 22 June 2022; Issue published 22 September 2022

Abstract

Increasing the life span and efficiency of Multiprocessor System on Chip (MPSoC) by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems. With the advancement of technology, the performance management of central processing unit (CPU) is changing. Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size. When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor (CMOS) circuits and reduces the speed by 10%–15% because excessive on-chip temperature shortens the chip’s life cycle. In this paper, we address the scheduling & energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling (EA-EDF) based technique for multiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor system-on-chip while lowering energy and power consumption. The selection of core and migration of tasks prevents the system from reaching its maximum energy utilization while effectively using the dynamic power management (DPM) policy. Increase in the execution of tasks the temperature and utilization factor on-chip increases that dissipate more power. The proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple CPUs. The performance of the EA-EDF algorithm was evaluated by an extensive set of experiments, where excellent results were reported when compared to other current techniques, the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7% on a utilization of 6%, 36% & 46% at 520 & 624 MHz operating frequency when particularly in comparison to other energy-aware methods for MPSoCs. Tasks are running and accurately scheduled to make an energy-efficient processor by controlling and managing the thermal effects on-chip and optimizing the energy consumption of MPSoCs.

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