THEAS: Efficient Power Management in Multi-Core CPUs via Cache-Aware Resource Scheduling
Abstract
The dynamic adaptation of resource levels enables the system to enhance energy efficiency while maintaining the necessary computational resources, particularly in scenarios where workloads fluctuate significantly over time. The proposed approach can play a crucial role in heterogeneous systems where workload characteristics are not uniformly distributed, such as non-pinning tasks. The deployed THEAS algorithm in this research work ensures a balance between performance and power consumption, making it suitable for a wide range of real-time applications. A comparative analysis of the proposed THEAS algorithm with well-known scheduling techniques such as Completely Fair Scheduler (CFS), Energy-Aware Scheduling (EAS), Heterogeneous Scheduling (HeteroSched), and Utility-Based Scheduling is presented in Table III. Each scheme is compared based on adaptability, core selection criteria, performance scaling, cache awareness, overhead, and real-time suitability.
Cite
@article{arxiv.2510.09847,
title = {THEAS: Efficient Power Management in Multi-Core CPUs via Cache-Aware Resource Scheduling},
author = {Said Muhammad and Lahlou Laaziz and Nadjia Kara and Phat Tan Nguyen and Timothy Murphy},
journal= {arXiv preprint arXiv:2510.09847},
year = {2025}
}
Comments
Accepted and presented at the 13th IEEE International Conference on Intelligent Mobile Computing 2025 (IMC), CISOSE 2025 in Tucson, Arizona, USA. This is the author's accepted manuscript (AAM). The final published version will appear in the IEEE conference proceedings