Divide and Save: Splitting Workload Among Containers in an Edge Device to Save Energy and Time

TL;DR

This paper proposes a workload splitting method among containers on edge devices to reduce energy consumption and computation time, demonstrated through experiments with Nvidia Jetson boards and a YOLO-based object detection task.

Contribution

It introduces a novel container-based workload distribution approach to improve energy efficiency and speed in edge computing environments.

Findings

Workload splitting reduces energy consumption.

Parallel processing speeds up inference tasks.

Significant savings demonstrated with YOLO on Nvidia Jetson boards.

Abstract

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.