Deep reinforcement learning-based joint caching and computing edge service placement for sensing-data-driven IIoT applications

Abstract

<div lang="en" class="abs"><p class="abs_header">Abstract</p><p>Edge computing (EC) is a promising technology to support a variety of performance-sensitive intelligent applications, especially in the Industrial Internet of Things (IIoT). The sensing-data-driven applications whose task processing requires sensing data from various sensors are typical applications in IIoT systems. The placement of caching and computing edge service functions for such applications is vital to ensure system performance and resource utilization in EC-enabled IIoT systems. Therefore, this paper investigates the joint caching and computing edge service placement (JCCESP) for multiple sensing-data-driven IIoT applications in an EC-enabled IIoT system. The JCCESP problem is formulated as a Markov Decision Process (MDP). Then, a deep reinforcement learning (DRL)-based approach is proposed to address the challenges like limited prior knowledge and the heterogeneity of such IIoT systems. Under such an approach, the policy network of the DRL agent is constructed based on an encoder-decoder model to tackle various applications requiring different numbers of service functions. A REINFORCE-based method is further employed to train the policy network. Simulation results indicate that the performances achieved by our proposed approach can converge after training and are significantly superior to benchmarks.</p></div>