A User-Oriented Redundancy Elimination Strategy In WLANs

Recent years, with the development of mobile Internet and the spreading of mobile smart equipments(laptops, smartphones, tablets), WLAN is widely considered as the next generation of networking and will replace wired network in the future. The data produced by smart equipments during daily life is becoming bigger and faster since the large volume of network files become increasing popular, such as videos, music and pictures. At the same time, the global mobile network grows in an astonishing speed, mobile Internet account for 13% of total networking data tra?c. The trend of transferring from traditional Internet service to mobile equipment are very clear now,for example, more than 50% of data tra?c of Twitter come from mobile equipments and smartphones account for 30% of the total network tra?c in Facebook. The development of mobile network is beyond imagination, the overall network tra?c grows at the rate from 100% to 200%.In the future, the development of mobile networking won’t meet the growth of mobile Internet tra?c demand. As a supplement to mobile network, WLAN will play a vital role in the mobile Internet times. But WLAN will meet the same challenge faced by mobile network such as highly mobile services, high volume data transferring and more intelligent smart services. We need more effective solutions to solve the problem raised by high volume tra?c other than blindly deploy more WLAN equipment.Researcher has proved that there exists tremendous redundant tra?c in wired network, but how to use this technology to eliminate redundant data in WLAN is still a question. How to reduce redundancy tra?c and improve tra?c e?ciency have been a research hotspot. In this paper, we collected read traces from over 10000 WiFi users from a company in Shanghai. Through intensive data analysis, we found that there exists more than 24% redundancy tra?c in WLAN. By digging the patterns of data set, we proposed a user-oriented redundancy elimination strategy which can allocate different cache size to different users adaptively. Simulation results shows that useroriented redundancy elimination strategy can detected and eliminate redundant tra?c effectively.