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<p>Edge computing faces challenges in energy-efficient task scheduling for heterogeneous multicore processors (HMPs). Existing solutions focus on reactive workload adaptation and energy prediction but fail to effectively integrate dynamic voltage and frequency scaling (DVFS). This paper proposes a novel algorithm integrating task prioritization, core-aware mapping, and predictive DVFS. Our approach outperforms state-of-the-art methods, reducing energy consumption by 20.9% while maintaining a low 2.4% deadline miss rate. Experiments on real HMP platforms demonstrate the algorithm’s scalability and adaptability to varying workloads. This work advances energy-efficient edge computing, balancing performance and power constraints.</p>

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Energy efficient task scheduling for heterogeneous multicore processors in edge computing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月7日

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  • Yanchun Liu,
  • Hongxue Qu,
  • Shuang Chen,
  • Xuejun Feng

摘要

Edge computing faces challenges in energy-efficient task scheduling for heterogeneous multicore processors (HMPs). Existing solutions focus on reactive workload adaptation and energy prediction but fail to effectively integrate dynamic voltage and frequency scaling (DVFS). This paper proposes a novel algorithm integrating task prioritization, core-aware mapping, and predictive DVFS. Our approach outperforms state-of-the-art methods, reducing energy consumption by 20.9% while maintaining a low 2.4% deadline miss rate. Experiments on real HMP platforms demonstrate the algorithm’s scalability and adaptability to varying workloads. This work advances energy-efficient edge computing, balancing performance and power constraints.