周钰,,
杨友超,
赵国繁,
陈前斌
1.重庆邮电大学通信与信息工程学院 ??重庆 ??400065
2.重庆邮电大学移动通信重点实验室 ??重庆 ??400065
基金项目:国家自然科学基金(61571073)
详细信息
作者简介:唐伦:男,1973年生,教授,博士生导师,主要研究方向为新一代无线通信网络、异构蜂窝网络、软件定义无线网络等
周钰:男,1993年生,硕士生,研究方向为5G网络切片资源分配和深度学习
杨友超:男,1993年生,硕士生,研究方向为网络虚拟化和切片资源分配
赵国繁:女,1993年生,硕士生,研究方向为5G网络切片中的资源分配,可靠性
陈前斌:男,1967年生,教授,博士生导师,主要研究方向为个人通信、多媒体信息处理与传输、下一代移动通信网络
通讯作者:周钰 137068966@qq.com
中图分类号:TN929.5计量
文章访问数:3965
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被引次数:0
出版历程
收稿日期:2018-09-18
修回日期:2019-02-20
网络出版日期:2019-03-21
刊出日期:2019-09-10
Virtual Network Function Dynamic Deployment Algorithm Based on Prediction for 5G Network Slicing
Lun TANG,Yu ZHOU,,
Youchao YANG,
Guofan ZHAO,
Qianbin CHEN
1. School of Communication and Information Engineering, Chongqing University of Post and Telecommunications, Chongqing 400065, China
2. Key Laboratory of Mobile Communication Technology, Chongqing University of Post and Telecommunications, Chongqing 400065, China
Funds:The National Natural Science Foundation of China (61571073)
摘要
摘要:针对无线虚拟化网络在时间域上业务请求的动态变化和信息反馈时延导致虚拟资源分配的不合理,该文提出一种基于长短时记忆(LSTM)网络的流量感知算法,该算法通过服务功能链(SFC)的历史队列信息来预测未来负载状态。基于预测的结果,联合考虑虚拟网络功能(VNF)的调度问题和相应的计算资源分配问题,提出一种基于最大最小蚁群算法(MMACA)的虚拟网络功能动态部署方法,在满足未来队列不溢出的最低资源需求的前提下,采用按需分配的方式最大化计算资源利用率。仿真结果表明,该文提出的基于LSTM神经网络预测模型能够获得很好的预测效果,实现了网络的在线监测;基于MMACA的VNF部署方法有效降低了比特丢失率的同时也降低了整体VNF调度产生的平均端到端时延。
关键词:5G网络切片/
资源分配/
流量感知/
预测/
虚拟网络功能F调度
Abstract:In order to solve the unreasonable virtual resource allocation caused by the dynamic change of service request and delay of information feedback in wireless virtualized network, a traffic-aware algorithm which exploits historical Service Function Chaining (SFC) queue information to predict future load state based on Long Short-Term Memory (LSTM) network is proposed. With the prediction results, the Virtual Network Function (VNF) deployment and the corresponding computing resource allocation problems are studied, and a VNFs’ deployment method based on Maximum and Minimum Ant Colony Algorithm (MMACA) is developed. On the premise of satisfying the minimum resource demand for future queue non-overflow, the on-demand allocation method is used to maximize the computing resource utilization. Simulation results show that the prediction model based on LSTM neural network in this paper obtains good prediction results and realizes online monitoring of the network. The Maximum and Minimum Ant Colony Algorithm based VNF deployment method reduces effectively the bit loss rate and the average end-to-end delay caused by overall VNFs’ scheduling at the same time.
Key words:5G network slicing/
Resource allocation/
Traffic-aware/
Prediction/
Virtual Network Function (VNF) scheduling
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