DNNOff: Offloading DNN-Based Intelligent IoT Applications in Mobile Edge Computing

Abstract

A deep neural network (DNN) has become increasingly popular in industrial Internet of Things scenarios. Due to high demands on computational capability, it is hard for DNN-based applications to directly run on intelligent end devices with limited resources. Computation offloading technology offers a feasible solution by offloading some computation-intensive tasks to the cloud or edges. Supporting such capability is not easy due to two aspects: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptability:</i> offloading should dynamically occur among computation nodes. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Effectiveness:</i> it needs to be determined which parts are worth offloading. This article proposes a novel approach, called DNNOff. For a given DNN-based application, DNNOff first rewrites the source code to implement a special program structure supporting on-demand offloading and, at runtime, automatically determines the offloading scheme. We evaluated DNNOff on a real-world intelligent application, with three DNN models. Our results show that, compared with other approaches, DNNOff saves response time by 12.4–66.6% on average.