1 / 20

Characterizing Geospatial Dynamics of Application Usage in a 3G Cellular Data Network

Characterizing Geospatial Dynamics of Application Usage in a 3G Cellular Data Network. M. Zubair Shafiq 1 , Lusheng Ji 2 , Alex X. Liu 1 , Jeffrey Pang 2 , Jia Wang 2 1 Michigan State University, East Lansing, MI 2 AT&T Labs – Research, Florham Park, NJ. 3/28/2012. Motivation (1/2).

roza
Download Presentation

Characterizing Geospatial Dynamics of Application Usage in a 3G Cellular Data Network

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Characterizing Geospatial Dynamics of Application Usage in a 3G Cellular Data Network M. Zubair Shafiq1, Lusheng Ji2, Alex X. Liu1, Jeffrey Pang2, Jia Wang2 1Michigan State University, East Lansing, MI 2AT&T Labs – Research, Florham Park, NJ 3/28/2012

  2. Motivation (1/2) • Cellular data has tripled for three years in a row (237 petabytes/month in 2010) [CISCO] • Explosive increase in the data traffic volume over cellular networks • Cellular operators have limited radio frequency spectrum • Need to optimize network planning and management to improve KPIs • How to optimize different dimensions? • Application mix? • Geo-location?

  3. Motivation (2/2) • A typical question? “Do cell sectors see dramatically different application mixes?” • Motivation: RRC transition timers trade-off radio-resources/energy and user response time • Interactive apps like web would like longer timers, but background streaming would like shorter timers • If answer is YES, then network operators should tune cells differently

  4. Agenda • Data • Network architecture • Data collection • Measurement • Aggregate analysis • Cell clustering • Geospatial analysis • Cluster composition analysis • Intensity function analysis • Conclusions

  5. Data

  6. Architecture Overview • RNCs in radio access network control transmission scheduling and handovers • GGSN in core network anchors IP Tunnel to UE using GPRS tunneling protocol (GTP)

  7. Data Collection (1/2) • Jointly study two anonymized data sets (1) From core network containing flow-level IP info. • Contains inaccurate location information from GTP (2) From radio network containing fine-grained location and handover information

  8. Data Collection (2/2) • Covers a large metropolitan area in the United States over the duration of 32 hours • Applications identified using port, HTTP host, and user-agent information, other heuristics • Contains protocol, class, “app” name if from “App Store” • Not just HTTP URLs, as in prior work • Example core network record: 123456789|UserID|tcp|moviesite.com|video_streaming|12345|6 • Example radio network record: 123456789|UserID|Location

  9. Measurement

  10. Aggregate Analysis (1/2) • Question: Do different applications enjoy same overall popularity? • Answer: No. • High volume applications have priority in network optimization web web email email streaming streaming

  11. Aggregate Analysis (2/2) • Question: Do different application enjoy same popularity at different locations? • How do define application popularity? (Byte, flow, users?) • Answer: No. • Web is most ubiquitous, dating is most scarce Byte volume Unique user count

  12. Cell Clustering (1/3) • Question: Every cell has a different distribution, how to conduct analysis? • Answer: Cluster application distributions of cells into a handful number of clusters • 19 element feature vector for each cell • [V1, V2, V3, …. , V19] • K-means clustering • Use Gap statistic to determine the suitable number of clusters

  13. Cell Clustering (2/3) • Cluster centroids for byte, packet, flow, and user distributions • Helps to identify cells with distinct traffic patterns Music & audio 8% cells Web browsing 36% cells

  14. Cell Clustering (3/3) Email 15% cells Streaming 11% cells MMS 6% cells Multiple 76% cells

  15. Cluster Composition Analysis • Question: Do different geographical regions have different application mixes? • Suburbs have more streaming and music use • Downtown and university have more web Cluster composition analysis for byte distributions

  16. Intensity Function Analysis (1/2) • Kernel estimated intensity function unbiased intensity estimator edge bias correction kernel function Flow Byte Packet User Intensity function for web browsing

  17. Intensity Function Analysis (2/2) • Question: Can we identify specific geographical areas with conflicting QoS requirements? • Take differences between intensity functions to identify Music & Audio is streaming, email is best effort Music & Audio – (email + web browsing)

  18. Summary and Implications • Application distributions significantly vary for byte, packet, flow, and user counts • Application mix significantly varies across neighborhoods (downtown, suburb, etc.) • The popularity of different applications varies even within a given neighborhood Geospatial correlations in application usage can be leveraged to optimize cellular network parameters for KPI improvement

  19. Questions?

  20. References • Data collection: J. Erman, A. Gerber, M. T. Hajiaghayi, D. Pei, and O. Spatscheck. Network-aware forward caching. In WWW, 2009. • Location information: Q. Xu, A. Gerber, Z. M. Mao, and J. Pang. AccuLoc: Practical localization of performance measurement in 3G networks. In ACM MobiSys, 2011. • Prior Work: I. Trestian, S. Ranjan, A. Kuzmanovic, and A. Nucci. Measuring serendipity: Connecting people, locations and interests in a mobile 3G network. In ACM IMC, 2009. • Prior Work: F. P. Tso, J. Teng, W. Jia, and D. Xuan. Mobility: A double-edged sword for HSPA networks. In ACM MobiHoc, 2010. • Prior Work: M. ZubairShafiq, LushengJi, Alex X. Liu, Jia Wang, Characterizing and Modeling Internet Traffic Dynamics of Cellular Devices, In ACM SIGMETRICS, 2011. • Prior Work: U. Paul, A. P. Subramanian, M. M. Buddhikot, and S. R. Das. Understanding traffic dynamics in cellular data networks. In IEEE Infocom, 2011.

More Related