Correlation Aware Scheduling for Edge-Enabled Industrial Internet of Things

Abstract

Industrial Internet of Things (IIoT) has attracted increasing attention for improving the efficiency of manufacturing. Plenty of computation-intensive and latency-sensitive applications are required by IIoT networks, which pose significant challenges for the computation capacities of IIoT networks. To address these challenges, Edge-enabled Industrial Internet of Things (E-IIoT) emerges. Edge devices located at the edge of IIoT networks enlarge computation capacities of IIoT networks and improve their efficiency accordingly. How to schedule computation resources wisely is a major problem in E-IIoT networks. Since IIoT devices in an E-IIoT network monitor the industrial site collaboratively, tasks for processing sensory data collected by them are correlated accordingly. That means, scheduling highly correlated tasks to be processed at the same device can improve computation efficiency. Inspired by this fact, we propose a correlation aware scheduling (CAS) algorithm for E-IIoT networks in this article. In specific, computation model decision and processing order decision are made by considering computation resources of devices and correlations among tasks in the algorithm to minimize latency of E-IIoT networks. The NP-hardness of correlation aware latency minimization scheduling problem in E-IIoT networks is first proved. Theoretical analysis on approximation ratio of the CAS algorithm is provided, and simulation results demonstrate the effectiveness of the proposed algorithm in reducing latency.