Edge Computing Scheduling Method for Related Tasks of Industrial Internet of Things

The Industrial Internet of Things (IIoT) generally uses cloud computing mode to process tasks. However, in cases of excessive task volume, a high workload will lead to significant processing delays. Edge computing, due to its ability to achieve load balancing and reduce processing latency, is an effective solution. Existing research mainly focuses on how to schedule multiple tasks and multiple edge nodes effectively but lacks consideration of the correlation between tasks. In view of the shortcomings of current work, this paper defines an edge computing scheduling problem for related tasks in IIoT scenario and proves that this problem is NP-Hard. A related task scheduling method is proposed based on a Directed Optimization Genetic Algorithm(DO-GA). Finally, an experimental environment is set up to perform a performance comparison analysis of the proposed algorithm. Experimental results show that, in terms of convergence, scheduling success rate and stability, the method proposed in this paper outperforms traditional Genetic Algorithm(GA) and Particle Swarm Optimization(PSO).