Optimization of AI-Empowered Edge Computing Node Resource Scheduling Strategies

Abstract

Edge computing decentralizes resources to the network edge to alleviate pressure on cloud infrastructure and reduce transmission latency. However, scheduling efficiency is hindered by the limited resources and inherent heterogeneity of edge computing nodes. AI possesses
excellent perception, learning and decision-making capabilities. It is the primary modality for improving the resource scheduling in edge computing nodes. This study comprehensively presents the research status of AI-enhanced resource scheduling in edge computing nodes, analyzes
the core frameworks and application scope of the existing improvement strategies, and explores current challenges and future development
trends, aiming to provide reference for relevant investigation and implementation initiatives.