How Matching Theory Enables Multi-access Edge Computing Adaptive Task Scheduling in IIoT

Fifth-generation mobile communication technology (5G) is a powerful driving force for the Industrial Internet of Things (IIoT). In the 5G-based IIoT, multi-access edge computing (MEC) can move traffic and service computing from the centralized cloud to the edge networks, thus, effectively improving the real-time performance of task processing. In this context, it is crucial to assign real-time tasks generated by numerous edge devices to MEC servers. Existing schemes usually schedule tasks in batches within time slots and ignore the situations where edge tasks arrive with time-varying density. However, the problem is that these schemes can lead to extra waiting delay in the slots with sparse tasks, thus, resulting in additional latency in task processing. To solve this problem, we propose a task scheduling scheme based on two-stage hybrid matching. The proposed scheme measures the time-varying density of tasks and switches between two stages: offline and online matching stages, according to the different task densities. Experimental results show that our scheme has a lower task execution time compared with other state-of-the-art schemes.