摘要
为应对高并发接入场景下通信网络中的流量拥塞与资源失衡问题,文章提出一种基于边缘计算的网络流量优化方法。该方法通过构建状态感知驱动的调度模型,结合节点资源向量与链路动态负载信息,设计任务优先级评分机制与路径代价函数并在边缘节点上部署多模块协同系统,实现任务的自适应分发与多路径负载均衡控制。在智能制造车间部署实验平台的验证结果显示:在并发任务数为400的条件下,该方法将任务平均完成时间控制在1.69 s,系统调度延迟不超过47.8 ms,链路利用率提升至83.2%,调度成功率保持在92.4%以上,较传统策略分别提升约29.2%、39.1%、11.7%与23.2%。实验结果表明,该方法在保障资源利用效率与时延性能方面具备良好的工程适配性。
In order to deal with the traffic congestion and resource imbalance in the communication network under the high concurrent access scenario,this paper proposes a network traffic optimization method based on edge computing.This method constructs a state aware driven scheduling model,combines node resource vectors and link dynamic load information,designs a task priority scoring mechanism and path cost function,and deploys a multi module collaborative system on edge nodes to achieve adaptive task distribution and multi path load balancing control.The verification results of deploying the experimental platform in the intelligent manufacturing workshop show that under the condition of 400 concurrent tasks,this method controls the average completion time of tasks to 1.69 s,the system scheduling delay does not exceed 47.8 ms,the link utilization rate is improved to 83.2%,and the scheduling success rate remains above 92.4%,which are about 29.2%,39.1%,11.7%,and 23.2%higher than traditional strategies,respectively.The experimental results show that this method has good engineering adaptability in ensuring resource utilization efficiency and latency performance.
作者
刘娜
LIU Na(Qinghai College of Architectural Technology,Xining 810012,China)
出处
《无线互联科技》
2025年第15期5-8,共4页
Wireless Internet Science and Technology
关键词
边缘计算
网络流量优化
任务调度
路径重构
状态感知
edge computing
network traffic optimization
task scheduling
path reconstruction
state awareness
