基于聚类算法内容流行度预测的空天地一体化网络缓存方法

仇超; 王晨阳

doi:10.11959/j.issn.2096-8930.2024005

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基于聚类算法内容流行度预测的空天地一体化网络缓存方法

专题：卫星互联网体系架构及相关技术 | 更新时间：2024-06-03

- 基于聚类算法内容流行度预测的空天地一体化网络缓存方法
- A Space-Air-Ground Integrated Network Caching Method Based on Clustering Algorithm Content Popularity Prediction
- 天地一体化信息网络 2024年5卷第1期页码：40-47
- 作者机构：
  
  1. 天津大学，天津 300072
  2. 深圳大学计算机与软件学院，广东深圳 518060
  3. 人工智能与数字经济广东省实验室（深圳），广东深圳 518132
- 作者简介：
  
  [ "仇超（1988− ），女，博士，天津大学副教授，主要研究方向为算力网络、边缘计算、边缘智能" ]
  [ "王晨阳（1989− ），男，博士，深圳大学计算机与软件学院博士后，主要研究方向为边缘计算、大数据分析、面向大模型的边缘智能" ]
- 基金信息：
  
  国家自然科学基金青年项目资助;The National Natural Science Foundation of China (Youth)(62002260)
- DOI：10.11959/j.issn.2096-8930.2024005
  中图分类号： TP393
- 网络出版日期：2024-03，
  
  纸质出版日期：2024-03-20
- 稿件说明：
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仇超, 王晨阳. 基于聚类算法内容流行度预测的空天地一体化网络缓存方法[J]. 天地一体化信息网络, 2024,5(1):40-47.

Chao QIU, Chenyang WANG. A Space-Air-Ground Integrated Network Caching Method Based on Clustering Algorithm Content Popularity Prediction[J]. Space-integrated-ground information networks, 2024, 5(1): 40-47.
仇超, 王晨阳. 基于聚类算法内容流行度预测的空天地一体化网络缓存方法[J]. 天地一体化信息网络, 2024,5(1):40-47. DOI： 10.11959/j.issn.2096-8930.2024005.

Chao QIU, Chenyang WANG. A Space-Air-Ground Integrated Network Caching Method Based on Clustering Algorithm Content Popularity Prediction[J]. Space-integrated-ground information networks, 2024, 5(1): 40-47. DOI： 10.11959/j.issn.2096-8930.2024005.

摘要

针对空天地一体化网络中恶劣的自然条件可能造成连通异常和通信阻塞的情况，对一种新的空天地一体化网络缓存方法进行研究，即基于聚类的内容缓存方法。基于空天地一体化网络架构，利用提取的内容特征来预测流行度，并用于缓存替换决策。内容的流行度预测是由动态聚类算法实现的，这种方法可以有效地减轻地面核心网的通信压力，从而进一步提升用户体验。使用开放数据集对算法进行评估，实验仿真结果证明了该方法的可行性和有效性。

Abstract

This paper proposed a new space-air-ground integrated network caching method

that was

cluster based content caching method.Based on the space-air-ground integrated network architecture

this method used the extracted content features to predict the popularity and used it for cache replacement decision.Content popularity prediction was realized based on dynamic clustering algorithm.This method could effectively reduce the pressure on the ground backbone network and further improve the quality of user experience.It evaluated the performance of our method using the open Douban movie review dataset.This dataset could be used to simulate user requests.Experimental simulation results showed the feasibility and effectiveness of the method.

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references

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