LAUSR

Fog computing has become a promising technology to improve the performance of low-powered Industrial Internet of Things (IIoT) devices by providing flexible and convenient computing services at the edge of the network with minimum delay. However, due to the ever-increasing traffic load in the network, the traditional service provisioning strategies can pose high complexity as well as network congestion, resulting in high energy consumption. Owing to this issue, in this article, we propose a deep reinforcement learning (DRL)-based service provisioning strategy in a software-defined industrial fog network to minimize the energy consumption of the network. The service provisioning strategy is performed in the network data plane, whereas the DRL is deployed in the control plane to enhance network efficiency. The service provisioning problem is formulated as the Markov decision process (MDP) and further solved by adopting the concept of a deep $Q$ network (DQN). Further, we propose a task migration policy to ensure the high availability of computing devices while meeting a single point of failure (SPOF) issue. Finally, to show the effectiveness of the proposed method, it is compared with the traditional baseline algorithms over various performance metrics.