Modeling cellular network traffic with mobile call graph constraints

The design, analysis and evaluation of protocols in cellular and hybrid networks requires realistic traffic modeling, since the underlying mobility and traffic model has a significant impact on the performance. We present a unified framework involving constrained temporal graphs that incorporate a variety of spatial, homophily and call-graph constraints into the network traffic model. The specific classes of constraints include bounds on the number of calls in given spatial regions, specific homophily relations between callers and callees, and the indegree and outdegree distributions of the call graph, for the whole time duration and intervals. Our framework allows us to capture a variety of complex behavioral adaptations and study their impacts on the network traffic. We illustrate this by a case study showing the impact of different homophily relations on the spatial and temporal characteristics of network traffic as well as the structure of the call graphs.

[1]  Madhav V. Marathe,et al.  Synthesis and Analysis of Spatio-Temporal Spectrum Demand Patterns: A First Principles Approach , 2010, 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN).

[2]  Stephan Eidenbenz,et al.  SessionSim: Activity-based session generation for network simulation , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[3]  Matthew O. Jackson,et al.  Average Distance, Diameter, and Clustering in Social Networks with Homophily , 2008, WINE.

[4]  Christos Faloutsos,et al.  Mobile call graphs: beyond power-law and lognormal distributions , 2008, KDD.

[5]  Sougata Mukherjea,et al.  Analyzing the Structure and Evolution of Massive Telecom Graphs , 2008, IEEE Transactions on Knowledge and Data Engineering.

[6]  Mary Baker,et al.  Analysis of a local-area wireless network , 2000, MobiCom '00.

[7]  A. Wolisz,et al.  Primary Users in Cellular Networks: A Large-Scale Measurement Study , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[8]  Madhav V. Marathe,et al.  Cascading failures in multiple infrastructures: From transportation to communication network , 2010, 2010 5th International Conference on Critical Infrastructure (CRIS).

[9]  Madhav V. Marathe,et al.  Implications of Dynamic Spectrum Access on the Efficiency of Primary Wireless Market , 2010, 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN).

[10]  Madhav V. Marathe,et al.  Generation and analysis of large synthetic social contact networks , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[11]  David Kotz,et al.  Analysis of a Campus-Wide Wireless Network , 2002, MobiCom '02.

[12]  Madhav V. Marathe,et al.  EpiSimdemics: An efficient algorithm for simulating the spread of infectious disease over large realistic social networks , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

[13]  Paramvir Bahl,et al.  Characterizing user behavior and network performance in a public wireless LAN , 2002, SIGMETRICS '02.