面向5G的云边端协同计算资源优化与任务调度研究  

Research on Cloud-Edge-End Collaborative Computing Resource Optimization and Task Scheduling for 5G

作　　者：屈定春 QU Dingchun(Gongcheng Management Consulting Co.,Ltd.,Guangzhou 516030,China)

机构地区：[1]公诚管理咨询有限公司,广东广州516030

出　　处：《数字通信世界》2024年第12期74-76,79,共4页Digital Communication World

摘　　要：该文针对“云-边-端”场景下资源分配与任务调度效果不佳的问题,提出基于权重综合成本模型的模拟退火算法优化策略。仿真实验表明,该策略能显著提升资源利用率、降低任务延迟和系统成本,对5G网络的资源分配与任务调度优化具有重要实用价值。This study addresses the issue of inefficient resource allocation and task scheduling in the"cloud-edge-end"scenario,proposing an optimization strategy based on a weighted comprehensive cost model and simulated annealing algorithm.Simulation experiments demonstrate that this strategy significantly improves resource utilization,reduces task latency,and lowers system costs,thus holding substantial practical value for resource allocation and task scheduling optimization in 5G networks.

关 键 词：多接入边缘计算 云边端协同 资源优化 任务调度 模拟退火算法 

分 类 号：TN929.53[电子电信—通信与信息系统] TP393[电子电信—信息与通信工程]