智能手机和移动设备的迅速普及,推动了企业级无线网络的广泛部署.通过收集上海某高校WiFi校园网中10000多位匿名用户的WiFi网络数据,发现无线局域网中存在超过高达24%的冗余数据传输.通过分析,挖掘无线用户数据的冗余特性,提出了一种面向用户的冗余数据消除机制.该机制可自适应地根据不同用户冗余度的动态变化,为不同的用户分配不同的缓存大小.从而在路由器缓存有限的条件下获得最大的冗余消除收益.通过基于真实历史数据的仿真实验,证实了该冗余消除机制能够有效识别并消除系统中的冗余数据,并获得较高的系统冗余消除收益.

The rapid spreading of mobile devices and smartphones has promoted the deployment of enterprise WLANs. This research collects read traces from over 10000 WiFi users in one university in Shanghai. Through intensive data analysis, it finds that there exists more than 24% redundancy traffic in WLANs. By mining the patterns of data set, a user-oriented redundancy elimination strategy is proposed to allocate different cache sizes to different users adaptively. Simulation results show that user-oriented redundancy elimination strategy can detect and eliminate redundant traffic effectively.

国家自然科学基金(61170237)