Joint DNN partitioning and task offloading in mobile edge computing via deep reinforcement learning

Abstract

As Artificial Intelligence (AI) becomes increasingly prevalent, Deep Neural Networks (DNNs) have become a crucial tool for developing and advancing AI applications. Considering limited computing and energy resources on mobile devices (MDs), it is a challenge to perform compute-intensive DNN tasks on MDs. To attack this challenge, mobile edge computing (MEC) provides a viable solution through DNN partitioning and task offloading. However, as the communication conditions between different devices change over time, DNN partitioning on different devices must also change synchronously. This is a dynamic process, which aggravates the complexity of DNN partitioning. In this paper, we delve into the issue of jointly optimizing energy and delay for DNN partitioning and task offloading in a dynamic MEC scenario where each MD and the server adopt the pre-trained DNNs for task inference. Taking advantage of the characteristics of DNN, we first propose a strategy for layered partitioning of DNN tasks to divide the task of each MD into subtasks that can be either processed on the MD or offloaded to the server for computation. Then, we formulate the trade-off between energy and delay as a joint optimization problem, which is further represented as a Markov decision process (MDP). To solve this, we design a DNN partitioning and task offloading (DPTO) algorithm utilizing deep reinforcement learning (DRL), which enables MDs to make optimal offloading decisions. Finally, experimental results demonstrate that our algorithm outperforms existing non-DRL and DRL algorithms with respect to processing delay and energy consumption, and can be applied to different DNN types.