Cloud–Edge Collaboration for Industrial Internet of Things: Scalable Neurocomputing and Rolling-Horizon Optimization
Qiyue Li, Lijuan Li, Zhi Liu, Wei Sun, Weitao Li, Jie Li, Wei Zhao
Abstract
Cloud–edge collaboration and edge intelligence have greatly driven the growth of the Industrial Internet of Things (IIoT). However, the jittery network delay and limited computational resources of edge servers make it difficult to meet the stringent latency requirements in IIoT, and so far there is no good solution to solve this problem. To this end, we introduce scalable neurocomputing, which provides neural networks with different utilities and computation resource requirements, to be deployed on edge servers of cloud–edge IIoT systems. We then optimize such systems by formulating data scheduling and system computational resource allocation as an infinite horizon optimization problem, considering that the data collection from end devices is an infinite long-term process. To solve this hard problem, we design a rolling prediction-optimization framework that transforms the infinite horizon problem into a truncated finite horizon optimization that maximizes the average system utility while satisfying the stringent delay constraints. We have conducted extensive simulations and built a prototype system, which verify the feasibility and performance of our proposed scheme.