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The increasing demand for edge computing is leading to a rise in energyconsumption from edge devices, which can have significant environmental andfinancial implications. To address this, in this paper we present a novelmethod to enhance the energy efficiency while speeding up computations bydistributing the workload among multiple containers in an edge device.Experiments are conducted on two Nvidia Jetson edge boards, the TX2 and the AGXOrin, exploring how using a different number of containers can affect theenergy consumption and the computational time for an inference task. Todemonstrate the effectiveness of our splitting approach, a video objectdetection task is conducted using an embedded version of the state-of-the-artYOLO algorithm, quantifying the energy and the time savings achieved comparedto doing the computations on a single container. The proposed method can helpmitigate the environmental and economic consequences of high energy consumptionin edge computing, by providing a more sustainable approach to managing theworkload of edge devices.

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