Task Scheduling Strategy of Logistics Cloud Robot Based on Edge Computing

Abstract

In the rapidly evolving domain of edge computing, efficient task scheduling emerges as a pivotal challenge due to the increasing complexity and volume of tasks. This study introduces a sophisticated dual-layer hybrid scheduling model that harnesses the strengths of Graph Neural Networks and Deep Reinforcement Learning to enhance the scheduling process. By simplifying task dependencies with Graph Neural Network at the upper layer and integrating Deep Reinforcement Learning with heuristic algorithms at the lower layer, this model optimally allocates tasks, significantly improving scheduling efficiency and reducing response times, particularly beneficial for logistics cloud robots operating in edge computing contexts. We validated the effectiveness of this innovative model through rigorous simulation experiments on the EdgeCloudSim platform, comparing its performance against traditional heuristic methods such as Shortest Job First, First Come First Serve and Heterogeneous Earliest Finish Time. The results confirm that our model consistently achieves superior task scheduling performance across various task volumes, effectively meeting the scheduling demands. This study demonstrates the effectiveness of integrating advanced machine learning techniques with heuristic algorithms to enhance task scheduling processes, making it particularly suitable for scenarios with high demands on response times. This approach not only facilitates more efficient task management but also aligns with the needs of modern edge computing applications, streamlining operations and boosting overall system performance.