Deep Reinforcement Learning for Scheduling in an Edge Computing‐Based Industrial Internet of Things

Abstract

The demand for improving productivity in manufacturing systems makes the industrial Internet of things (IIoT) an important research area spawned by the Internet of things (IoT). In IIoT systems, there is an increasing demand for different types of industrial equipment to exchange stream data with different delays. Communications between massive heterogeneous industrial devices and clouds will cause high latency and require high network bandwidth. The introduction of edge computing in the IIoT can address unacceptable processing latency and reduce the heavy link burden. However, the limited resources in edge computing servers are one of the difficulties in formulating communication scheduling and resource allocation strategies. In this article, we use deep reinforcement learning (DRL) to solve the scheduling problem in edge computing to improve the quality of services provided to users in IIoT applications. First, we propose a hierarchical scheduling model considering the central-edge computing heterogeneous architecture. Then, according to the model characteristics, a deep intelligent scheduling algorithm (DISA) based on a double deep Q network (DDQN) framework is proposed to make scheduling decisions for communication. We compare DISA with other baseline solutions using various performance metrics. Simulation results show that the proposed algorithm is more effective than other baseline algorithms.

An Algorithm of Mobile Edge Computing Offloading Based on Improved SPEA2 Algorithm

“…Author details 1 School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, China. 2 Artificial Intelligence Key Laboratory of Sichuan Province, China.…”

“…With the growth of the number of smart mobile devices and the popularization of 5G network technology，the scale of the Internet of things (IoT) is expanding rapidly [1]. The application requirement of the Internet of things is also improved [2].…”

A Fairness of Data Combination in Wireless Packet Scheduling

“…In recent years, there has been major breakthroughs in the field of wireless communications using AI technology [13][14][15]. Some of the works which have considered AI to solve packet scheduling problems in wireless network systems are listed in [16][17][18][19]. In [16], a support vector machine (SVM) model-based packet scheduling in wireless communication networks has been proposed.…”

“…In [18], the joint subcarrier and power allocation problem is studied, where a deep Q-network is employed for scheduling decision making and a DNN is trained for power allocation using supervised learning. In [19], deep reinforcement learning (DRL)-based double-deep Q network (DDQN) framework is proposed to make scheduling decisions in the edge computing environment. However, the works mentioned in [16][17][18][19] more focused on increasing overall system throughput and did not guarantee the selection of UEs having low channel strength.…”

“…In [19], deep reinforcement learning (DRL)-based double-deep Q network (DDQN) framework is proposed to make scheduling decisions in the edge computing environment. However, the works mentioned in [16][17][18][19] more focused on increasing overall system throughput and did not guarantee the selection of UEs having low channel strength.…”