基于协作小小区与流行度预测的在线热点视频缓存更新策略

针对无线移动设备数量的指数增长使得异构协作小小区(SBS)将承载大规模的流量负载问题,提出了一种基于协作SBS与流行度预测的在线热点视频缓存更新方案(OVCRP)。首先,分析在线热点视频的流行度在短期内变化情况;然后,构建k近邻模型进行在线热点视频流行度的预测;最后,确定在线热点视频的缓存更新位置。为了选择合适的位置存放在线热点视频,以最小化总体传输时延为目标,建立数学模型,设计整数规划优化算法。仿真实验结果显示,与随机缓存(RANDOM)、最近最少使用(LRU)、最不经常使用(LFU)方案相比,OVCRP在平均缓存命中率和平均访问时延方面具有明显的优势,因此减轻了协作SBS的网络负担。

Online-hot video cache replacement policy based on cooperative small base stations and popularity prediction

The exponential growth in the number of wireless mobile devices leads that heterogeneous cooperative Small Base Stations (SBS) carry large-scale traffic load. Aiming at this problem, an Online-hot Video Cache Replacement Policy (OVCRP) based on cooperative SBS and popularity prediction was proposed. Firstly, the changes of popularity in short term of online-hot videos were analyzed, then a k-nearest neighbor model was constructed to predict the popularities of the online-hot videos, and finally the locations for cache replacement of online-hot videos were determined. In order to select appropriate locations to cache the online-hot videos, with minimization of overall transmission delay as the goal, a mathematical model was built and an integer programming optimization algorithm was designed. The simulation experiment results show that compared with the schemes such as RANDOM cache (RANDOM), Least Recently Used (LRU) and Least Frequently Used (LFU), the proposed OVCRP has obvious advantages in average cache hit rate and average access delay, reducing the network burden of cooperative SBS.