The increasing demand for edge computing is leading to a rise in energy consumption from edge devices, which can have significant environmental and financial implications. To address this, in this paper we present a novel method to enhance the energy efficiency while speeding up computations by distributing the workload among multiple containers in an edge device. Experiments are conducted on two Nvidia Jetson edge boards, the TX2 and the AGX Orin, exploring how using a different number of containers can affect the energy consumption and the computational time for an inference task. To demonstrate the effectiveness of our splitting approach, a video object detection task is conducted using an embedded version of the state-of-the-art YOLO algorithm, quantifying the energy and the time savings achieved compared to doing the computations on a single container. The proposed method can help mitigate the environmental and economic consequences of high energy consumption in edge computing, by providing a more sustainable approach to managing the workload of edge devices.

翻译：边缘计算需求的增长导致边缘设备能耗上升，这可能带来显著的环境与经济影响。为解决这一问题，本文提出一种新颖方法，通过将工作负载分布至边缘设备内的多个容器，在提升能效的同时加速计算。我们在两款Nvidia Jetson边缘开发板（TX2与AGX Orin）上开展实验，探究不同容器数量如何影响推理任务的能耗与计算时间。为验证所提分割方法的有效性，采用基于最新YOLO算法的嵌入式版本执行视频目标检测任务，量化了相较于单容器计算在能耗与时间上的节约。该方法通过提供更可持续的边缘设备工作负载管理方案，有助于缓解边缘计算高能耗带来的环境与经济后果。