分割与保存：在边缘设备中将工作负载分配到容器中以节省能源和时间
Divide and Save: Splitting Workload Among Containers in an Edge Device to Save Energy and Time
AI总结：
本文提出在边缘设备中通过容器分配工作负载以提高能效和计算速度，通过实验验证了该方法在能耗和时间上的节省效果。
摘要：
随着边缘计算需求的增加，边缘设备的能耗正上升，这可能带来显著的环境和经济影响。为了解决这一问题，本文提出了一种新颖的方法，通过在边缘设备中将工作负载分配到多个容器中，来提高能效并加快计算速度。实验在两块Nvidia Jetson边缘板（TX2和AGX Orin）上进行，探讨使用不同数量的容器对推理任务的能耗和计算时间的影响。为了展示我们分割方法的有效性，使用嵌入式版本的最新YOLO算法进行了视频目标检测任务，量化了与在单个容器中进行计算相比所实现的能耗和时间节省。所提出的方法可以通过提供更可持续的边缘设备工作负载管理方法，来缓解高能耗对边缘计算的环境和经济影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
