Cloud–Edge Collaborative SFC Mapping for Industrial IoT Using Deep Reinforcement Learning
Siya Xu, Yimin Li, Shaoyong Guo, Chenghao Lei, Di Liu, Xuesong Qiu
Abstract
The industrial Internet of Things (IIoT) and 5G have been served as the key elements to support the reliable and efficient operation of Industry 4.0. By integrating burgeoning network function virtualization (NFV) technology with cloud computing and mobile edge computing, an NFV-enabled cloud–edge collaborative IIoT architecture can efficiently provide flexible service for the massive IIoT traffic in the form of a service function chain (SFC). However, the efficient cloud–edge collaboration, the reasonable comprehensive resource consumption, and different quality of services are still key problems to be solved. Thus, to balance the quality of IIoT services, as well as computational and communicational resource consumption, a multiobjective SFC deployment model is designed to characterize the diverse service requirements and specific network environment for the IIoT. Then, a deep- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning-based online SFC deployment algorithm is presented, which can efficiently learn the relationship between the SFC deployment scheme and its performance through the iterative training. Simulation results demonstrate that our proposed approach outperforms others in balancing the resource consumption, accepting more SFC requests, as well as providing differentiated services for delay-sensitive IIoT traffic and resource-intensive IIoT traffic.