A modified poisson distribution for smartphone background traffic in cellular networks

Summary For the emerging applications such as Google Talk, Facebook, Skype and QQ, to mention a few, which run on smartphones, background traffic has become one of the significant issues in system design and optimization. Because of the complicated user behavior and interaction, the assumptions underlying the Poisson process model cannot be met; the Poisson distribution cannot approximate the distribution of background traffic arrivals accurately. In this paper, we propose a model, which can better fit the background traffic arrivals of smartphones than the Poisson distribution. The proposed model is a linear transformation of the Poisson distribution and is specified by three parameters, (a,b,λ), which can be estimated from the measured sample's mean, variance, and third central moment. Simulation results have corroborated the fitness of the proposed model in both single and mixed applications scenarios. In addition, we have also observed that the normalized parameters, (a,b0,λ0), of each application is independent of the user number and completely characterized by the type of application. Hence, with the given trace cumulative distribution functions of all applications, the proposed modified Poisson distribution can be used as a tool for modeling and analyzing background traffic arrivals with arbitrary user numbers and mixed applications. Copyright © 2016 John Wiley & Sons, Ltd.

[1]  Katia Obraczka,et al.  Collision-free medium access based on traffic forecasting , 2012, 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[2]  D. Everitt,et al.  On the teletraffic capacity of CDMA cellular networks , 1999 .

[3]  Maruti Gupta,et al.  Energy impact of emerging mobile internet applications on LTE networks: issues and solutions , 2013, IEEE Communications Magazine.

[4]  B. Melamed,et al.  Traffic modeling for telecommunications networks , 1994, IEEE Communications Magazine.

[5]  Christian Callegari,et al.  Skype-Hunter: A real-time system for the detection and classification of Skype traffic , 2012, Int. J. Commun. Syst..

[6]  Solon Venâncio de Carvalho,et al.  A queuing model for distributed scheduling in IEEE 802.16 wireless mesh networks , 2015, Int. J. Commun. Syst..

[7]  Zhuyan Zhao,et al.  Statistics of RRC state transition caused by the background traffic in LTE networks , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[8]  Polychronis Koutsakis,et al.  A New Call Admission Control Mechanism for Multimedia Traffic over Next-Generation Wireless Cellular Networks , 2008, IEEE Transactions on Mobile Computing.

[9]  Polychronis Koutsakis,et al.  Dynamic versus Static Traffic Policing: A New Approach for Videoconference Traffic over Wireless Cellular Networks , 2009, IEEE Transactions on Mobile Computing.

[10]  Mohammad Reza Aref,et al.  A flexible dynamic traffic model for reverse link CDMA cellular networks , 2004, IEEE Transactions on Wireless Communications.

[11]  Stephen S. Rappaport,et al.  Traffic model and performance analysis for cellular mobile radio telephone systems with prioritized and nonprioritized handoff procedures , 1986, IEEE Transactions on Vehicular Technology.

[12]  M. Menth,et al.  Source Models for Speech Traffic Revisited , 2009, IEEE/ACM Transactions on Networking.

[13]  Michael L. Honig,et al.  Resource allocation for multiple classes of DS-CDMA traffic , 2000, IEEE Trans. Veh. Technol..

[14]  Sanguo Zhang,et al.  Recognizing and characterizing dynamics of cellular devices in cellular data network through massive data analysis , 2015, Int. J. Commun. Syst..

[15]  Seong Gon Choi,et al.  Battery lifetime extension method by using background traffic synchronization , 2014, 16th International Conference on Advanced Communication Technology.

[16]  Chuanyi Ji,et al.  Modeling heterogeneous network traffic in wavelet domain , 2001, TNET.

[17]  Geyong Min,et al.  Modelling and Analysis of an Integrated Scheduling Scheme with Heterogeneous LRD and SRD Traffic , 2013, IEEE Transactions on Wireless Communications.

[18]  Bo Friis Nielsen,et al.  A Markovian approach for modeling packet traffic with long-range dependence , 1998, IEEE J. Sel. Areas Commun..

[19]  S. K. Baghel,et al.  An investigation into traffic analysis for diverse data applications on smartphones , 2012, 2012 National Conference on Communications (NCC).

[20]  Honggang Zhang,et al.  Spatial modeling of the traffic density in cellular networks , 2014, IEEE Wireless Communications.

[21]  Teong Chee Chuah,et al.  QoS-based radio network dimensioning for LTE networks with heavy real-time traffic , 2014, Int. J. Commun. Syst..

[22]  Armand M. Makowski,et al.  Modeling video traffic using M/G/∞ input processes: a compromise between Markovian and LRD models , 1998, IEEE J. Sel. Areas Commun..

[23]  Hui-Nien Hung,et al.  Modeling UMTS Power Saving with Bursty Packet Data Traffic , 2007, IEEE Transactions on Mobile Computing.

[24]  L. Charoenwatana,et al.  Examining the network traffic of facebook homepage retrieval: An end user perspective , 2012, 2012 Ninth International Conference on Computer Science and Software Engineering (JCSSE).

[25]  H. Okamura,et al.  Markovian Arrival Process Parameter Estimation With Group Data , 2009, IEEE/ACM Transactions on Networking.

[26]  Zhenlong Yuan,et al.  PBC: A novel method for identifying QQ traffic , 2014, 2014 International Conference on Computing, Networking and Communications (ICNC).