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：随着对边缘计算的需求不断增加，从边缘设备消耗的能量也在不断增加，这可能会对环境和财务造成重大影响。为了应对这一问题，在本文中，我们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，以增强能源效率同时加快计算速度。我们在两个英伟达Jetson边缘板上进行了实验，其中包括TX2和AGX Orin，探讨了使用不同数量的容器如何影响基于推理任务的能耗和计算时间。为了展示我们的分割方法的有效性，我们使用了嵌入式版本的最先进的Yolo算法进行视频目标检测任务，并与在单个容器上执行计算相比，量化了节约的能量和时间。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，帮助缓解边缘计算高能耗可能带来的环境和经济后果。