Multitask Multiobjective Deep Reinforcement Learning-Based Computation Offloading Method for Industrial Internet of Things

Abstract

Edge computing has emerged as a promising paradigm to deploy computing resources to the network edge. However, most existing computation offloading strategies consider only one objective, including latency, energy consumption, and weighted sum of latency and energy consumption. It is challenging to meet different requirements of the heterogeneous Industrial Internet of Things (IIoT) systems, simultaneously. To address this challenge, a multiagent deep reinforcement learning (MADRL)-based computation offloading method is proposed for cloud–edge–device computing, which aims to meet various requirements of different tasks. In the proposed model, two typical types of tasks are considered: 1) latency-sensitive tasks and 2) energy-sensitive tasks. Each type of task can be executed in one of the three layers, i.e., cloud, edge, or device layer. In addition, in the MADRL model, two agents are defined to make global offloading decisions for the two types of tasks according to the task characteristics and network resource status. The experimental results show that the proposed model can guarantee the quality of service in a heterogeneous IIoT system and achieve better system performance in terms of latency and energy consumption than weighted-sum optimization methods.