基于需求预测的云无线接入网计算资源分配策略研究

王志朋; 曹斌; 张钦宇

doi:10.11959/j.issn.2096-3750.2019.00126

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基于需求预测的云无线接入网计算资源分配策略研究

理论与技术 | 更新时间：2024-06-05

- 基于需求预测的云无线接入网计算资源分配策略研究
- Research on computing resource allocation strategy for cloud radio access network based on demand forecasting
- 物联网学报 2019年3卷第4期页码：1-8
- 作者机构：
  
  1. 哈尔滨工业大学（深圳）电子与信息工程学院，广东深圳 518055
  2. 鹏城实验室网络通信研究中心，广东深圳 518055
- 作者简介：
  
  [ "王志朋（1994- ），男，河南周口人，哈尔滨工业大学（深圳）电子与信息工程学院硕士生，主要研究方向为网络切片、云无线接入网和资源分配" ]
  [ "曹斌（1985- ），男，江西景德镇人，博士，哈尔滨工业大学（深圳）电子与信息工程学院党委副书记、副教授，主要研究方向为无线通信和网络、通信信号处理" ]
  [ "张钦宇（1972- ），男，江苏扬州人，博士，哈尔滨工业大学（深圳）电子与信息工程学院院长、教授，主要研究方向为空间通信和无线通信等" ]
- 基金信息：
  
  广东省自然科学基金资助项目;NSF Guangdong(2017A030313372);深圳市科技计划项目;The Basic Research Project of Shenzhen(JCYJ20170307151148585);海洋立体通信网络示范验证平台项目;Stereoscopic Coverage Communication Network Verification Platform for China Sea(PCL2018KP002)
- DOI：10.11959/j.issn.2096-3750.2019.00126
  中图分类号： TN929.5
- 纸质出版日期：2019-12-30，
  
  网络出版日期：2019-09，
- 稿件说明：
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王志朋, 曹斌, 张钦宇. 基于需求预测的云无线接入网计算资源分配策略研究[J]. 物联网学报, 2019,3(4):1-8.

ZHIPENG WANG, BIN CAO, QINYU ZHANG. Research on computing resource allocation strategy for cloud radio access network based on demand forecasting. [J]. Chinese journal on internet of things, 2019, 3(4): 1-8.
王志朋, 曹斌, 张钦宇. 基于需求预测的云无线接入网计算资源分配策略研究[J]. 物联网学报, 2019,3(4):1-8. DOI： 10.11959/j.issn.2096-3750.2019.00126.

ZHIPENG WANG, BIN CAO, QINYU ZHANG. Research on computing resource allocation strategy for cloud radio access network based on demand forecasting. [J]. Chinese journal on internet of things, 2019, 3(4): 1-8. DOI： 10.11959/j.issn.2096-3750.2019.00126.

摘要

云无线接入网利用网络功能虚拟化和软件定义网络技术以支持端到端的网络切片，使得接入网可共享无线、终端和网络等资源，已成为5G网络中优先采用的网络架构。针对云无线接入网中端到端的网络切片场景，通过控制平面建立的数据驱动运维框架来收集网络信息并进行数据处理。预测未来一段时间内计算业务量的需求，设计了一种基于虚拟化网络功能（VNF）的计算资源分配方案，提出了基于降序最佳适应（BFD）的离散粒子群算法。仿真结果表明，所提出的策略和算法可实现云无线接入网计算资源的动态灵活分配，并能有效降低VNF迁移能耗和迁移次数。

Abstract

Cloud radio access network(CRAN) is a new network architecture commonly used in 5G network.It uses network function virtualization and software defined network technology to support end-to-end network slicing so that access network can share the same infrastructure.A computing resource allocation scheme based on virtualized network function(VNF) computing resource demand forecasting was proposed for cloud wireless network supporting end-to-end network slicing.Data-driven operation and maintenance framework established by the control plane collects network information and performs data processing to predict the demand for computing traffic in a period of time

which used the discrete particle swarm optimization based on best-fit-decreasing to dynamically and dynamically allocate CRAN computing resources

reducing the migration energy consumption and migration times of VNF.

关键词

云无线接入网计算资源分配离散粒子群算法网络切片

Keywords

cloud radio access networkcomputing resource allocationdiscrete particle swarm optimizationnetwork slicing

references

杨靖, 张祖伟, 姚道远 ,等. 新型智慧城市全面感知体系[J]. 物联网学报, 2018,2(3): 95-101.

YANG J, ZHANG Z W, YAO D Y ,et al. Comprehensive sensing system of new smart city[J]. Chinese Journal on Internet of Things, 2018,2(3): 95-101.

中国移动通信有限公司研究院.迈向5G C-RAN:需求、架构与挑战白皮书[R]. 2016.

Research Institute of China Mobile Communication Co.,Ltd.. Towards 5G C-RAN:demand,architecture and challenge white paper[R]. 2016.

3GPP.Study on management aspects of virtualized network functions that are part of the New Radio (NR)[S]. 2018.

ZHANG F, ZHENG J, ZHANG Y ,et al. An efficient and balanced BBU computing resource allocation algorithm for cloud radio access networks[C]// 2017 IEEE 85th Vehicular Technology Conference (VTC Spring). IEEE, 2017: 1-5.

LYAZIDI M Y, AITSAADI N, LANGAR R . Dynamic resource allocation for Cloud-RAN in LTE with real-time BBU/RRH assignment[C]// 2016 IEEE International Conference on Communications (ICC). IEEE, 2016: 1-6.

WANG K, ZHOU W, MAO S . On joint BBU/RRH resource allocation in heterogeneous cloud-RANs[J]. IEEE Internet of Things Journal, 2017,4(3): 749-759.

AQEELI E, MOUBAYED A, SHAMI A . Power-aware optimized RRH to BBU allocation in C-RAN[J]. IEEE Transactions on Wireless Communications, 2017,(99):1.

YOUSAF F Z, GRAMAGLIA M, FRIDERIKOS V ,et al. Network slicing with flexible mobility and QoS/QoE support for 5G Networks[C]// IEEE International Conference on Communications Workshops(ICC Workshops). IEEE, 2017: 1195-1201.

Study on access traffic steering,switching and splitting support in the 5G system architecture[S]. 2017.

AFOLABI I, TALEB T, SAMDANIS K ,et al. Network slicing ＆softwarization:a survey on principles,enabling technologies ＆ solutions[J]. IEEE Communications Surveys ＆ Tutorials, 2018,(99):1.

SMOLA A J, SCHOLKOPF B . A tutorial on support vector regression[J]. Statistics and Computing, 2004,14(3):199.

PAN Q K, TASGETIREN M F, LIANG Y C . A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem[J]. Computers and Operations Research, 2008,35(9): 2807-2839.

LIU H, XU C Z, JIN H ,et al. Performance and energy modeling for live migration of virtual machines[C]// ACM 20th International Symposium on High Performance Distributed Computing (HPDC’11). ACM, 2011: 171-182.

BOGOMOLOV A, LEPRI B, LARCHER R ,et al. Energy consumption prediction using people dynamics derived from cellular network data[J]. EPJ Data Science, 2016,5(1):13.

JIN Y Q, XU X D, WANG Y T ,et al. Multi-QoS mobile services guaranteed resource allocation with effective capacity[C]// 2017 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2017: 1-6.

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