AccuLoc: practical localization of performance measurements in 3G networks

• Authors: Qiang Xu;Alexandre Gerber;Zhuoqing Morley Mao;Jeffrey Pang
• Venue: MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
• Year: 2011

Operators of 3G data networks need to distinguish the performance of each geographic area in their 3G networks to detect and resolve local network problems. This is because the quality of the "last mile" radio link between 3G base stations and end-user devices is a crucial factor in the end-to-end performance that each user experiences. It is relatively straightforward to measure the performance of all IP traffic in the 3G network from a small number of vantage points in the core network. However, the location information available about each mobile device (e.g., the cell sector/site that it is in) is often too stale to be accurate because of user mobility. Moreover, very costly infrastructure deployment and maintenance of custom equipment would be required to collect fine-grained location information about all mobile devices on an on-going basis in large 3G networks. Thus, it is a challenge to accurately assign IP performance measurements to fine-grained geographic regions of the 3G network using existing standard network components. Fortunately, previous studies have observed that human mobility patterns are very predictable. In this paper, we exploit this predictability to develop a novel clustering algorithm grouping related cell sectors that accurately assigns IP performance measurements to fine-grained geographic regions. We present results from a prototype in a real 3G network that shows our approach provides more accurate performance localization than existing approaches. Eventually, we can either narrow down individual IP performance measurements into only 4 candidate cell sectors consistently with the accuracy of 70% over one week based on a one-day snapshot of fine-grained 3GPP events, or increase the accuracy 20% comparing with site-level accuracy through lightweight handover statistics hourly collected at RNCs. Using our approach, we improve anomaly detection based on IP performance measurements by reducing the number of false positives and false negatives. Our study also sheds light on the mobility patterns of 3G devices.