research-article

Understanding the characteristics of cellular data traffic

Authors:

Ake ÅrvidssonAuthors Info & Claims

ACM SIGCOMM Computer Communication Review, Volume 42, Issue 4

Pages 461 - 466

https://doi.org/10.1145/2377677.2377764

Published: 24 September 2012 Publication History

Abstract

Because of rapidly growing subscriber populations, advances in cellular communication technology, increasingly capable user terminals, and the expanding range of mobile applications, cellular networks have experienced a significant increase in data traffic, the dominant part of which is carried by the http protocol. Understanding the characteristics of this traffic is important for network design, traffic modeling, resource planning and network control. In this study we present a comprehensive characterization study of mobile http-based traffic using packet level traces collected in a large cellular network. We analyze the traffic using metrics at packet level, flow level and session level. For each metric, we conduct a comparison between traffic from different applications, as well as comparison to traffic in a wired network. Finally, we discuss the implications of our findings for better resource utilization in cellular infrastructures.

References

[1]

M. F. Arlitt and C. L. Williamson. Internet web servers: workload characterization and performance implications. IEEE/ACM Trans. Netw., 5(5):631--645, October 1997.

Digital Library

[2]

AT&T. AT&T SXSW Press Release. www.att.com/Common/docs/SXSW\_Network%20 Fact\_Sheet.doc, 2011.

[3]

P. Barford, A. Bestavros, A. Bradley, and M. Crovella. Changes in web client access patterns: Characteristics and caching implications. World Wide Web, 2(1--2):15--28, January 1999.

Digital Library

[4]

P. Barford and M. Crovella. Generating representative web workloads for network and server performance evaluation. SIGMETRICS Perform. Eval. Rev., 26(1), 1998.

Digital Library

[5]

L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker. Web caching and zipf-like distributions: Evidence and implications. In INFOCOM'99, pages 126--134, 1999.

[6]

H.-K. Choi and J. O. Limb. A behavioral model of web traffic. In Proc. International Conference on Network Protocols, 1999.

Digital Library

[7]

C. Cunha, A. Bestavros, and M. Crovella. Characteristics of www client-based traces. Technical report, Boston, MA, USA, 1995.

Digital Library

[8]

J. Erman, A. Gerber, K. K. Ramadrishnan, S. Sen, and O. Spatscheck. Over the top video: the gorilla in cellular networks. In Proc. ACM SIGCOMM IMC, pages 127--136, 2011.

Digital Library

[9]

A. Feldmann. Blt: Bi-layer tracing of http and tcp/ip. In Proceedings of the 9th international World Wide Web conference on Computer networks: the international journal of computer and telecommunications networking, page 321--335, Amsterdam, The Netherlands, The Netherlands, 2000. North-Holland Publishing Co.

Digital Library

[10]

J. Huang, Q. Xu, B. Tiwana, Z. M. Mao, M. Zhang, and P. Bahl. Anatomizing application performance differences on smartphones. In Proceedings of the 8th international conference on Mobile systems, applications, and services, pages 165--178, 2010.

Digital Library

[11]

R. A. Kalden. Mobile internet traffic measurement and modeling based on data from commercial GPRS networks. PhD thesis, Enschede, 2004.

[12]

S. Lloyd. K-means clustering. http://en.wikipedia.org/wiki/K-means\_clustering, 1957.

[13]

T. W. MacFarland. Mann-Whitney U-Test. http://en.wikipedia.org/wiki/Mann%E2%80%93Whitney\_U, 1947.

[14]

B. A. Mah. An empirical model of http network traffic. In Proc. IEEE INFOCOM, 1997.

Digital Library

[15]

U. K. Paul, A. P. Subramanian, M. M. Buddhikot, and S. R. Das. Understanding traffic dynamic in cellular data networks. In Proc. IEEE INFOCOM, 2011.

[16]

K. Pearson. Principal Component Analysis. http://en.wikipedia.org/wiki/Principal\_component\_analysis, 1901.

[17]

F. Qian, Z. Wang, A. Gerber, Z. Mao, S. Sen, and O. Spatscheck. Profiling resource usage for mobile applications: a cross-layer approach. In Proceedings of the 9th international conference on Mobile systems, applications, and services, 2011.

Digital Library

[18]

M. Z. Shafiq, L. Ji, A. X. Liu, and J. Wang. Characterizing and modeling internet traffic dynamics of cellular devices. In Proc. ACM SIGMETRICS, pages 305--316, 2011.

Digital Library

[19]

F. D. Smith, F. H. Campos, K. Jeffay, and D. Ott. What tcp/ip protocol headers can tell us about the web. SIGMETRICS Perform. Eval. Rev., 29(1), 2001.

Digital Library

[20]

A. Wolman, M. Voelker, N. Sharma, N. Cardwell, A. Karlin, and H. M. Levy. On the scale and performance of cooperative web proxy caching. SIGOPS Oper. Syst. Rev., 33(5), December 1999.

Digital Library

[21]

Q. Xu, J. Erman, A. Gerber, Z. Mao, J. Pang, and S. Venkataraman. Identifying diverse usage behaviors of smartphone apps. In Proc. ACM SIGCOMM IMC, pages 329--344, 2011.

Digital Library

[22]

L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R. P. Dick, Z. M. Mao, and L. Yang. Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis, 2010.

Digital Library

Cited By

Mishra SZanella AMartínez-Durive OMadariaga DZiemlicki CFiore M(2024)Characterizing 5G Adoption and its Impact on Network Traffic and Mobile Service ConsumptionIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621344(1531-1540)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621344
Kim JLee YLim HJung YKim SHan DBianchi GMei A(2022)OutRANProceedings of the 18th International Conference on emerging Networking EXperiments and Technologies10.1145/3555050.3569122(369-385)Online publication date: 30-Nov-2022
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Chang WChen YWang P(2022)Hotspot Mitigation for Mobile Edge ComputingIEEE Transactions on Sustainable Computing10.1109/TSUSC.2018.28784387:2(313-323)Online publication date: 1-Apr-2022
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Index Terms

Understanding the characteristics of cellular data traffic
1. Networks
  1. Network services

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Published In

cover image ACM SIGCOMM Computer Communication Review

ACM SIGCOMM Computer Communication Review Volume 42, Issue 4

Special october issue SIGCOMM '12

October 2012

538 pages

ISSN:0146-4833

DOI:10.1145/2377677

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Copyright © 2012 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 September 2012

Published in SIGCOMM-CCR Volume 42, Issue 4

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Cited By

Mishra SZanella AMartínez-Durive OMadariaga DZiemlicki CFiore M(2024)Characterizing 5G Adoption and its Impact on Network Traffic and Mobile Service ConsumptionIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621344(1531-1540)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621344
Kim JLee YLim HJung YKim SHan DBianchi GMei A(2022)OutRANProceedings of the 18th International Conference on emerging Networking EXperiments and Technologies10.1145/3555050.3569122(369-385)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1145/3555050.3569122
Chang WChen YWang P(2022)Hotspot Mitigation for Mobile Edge ComputingIEEE Transactions on Sustainable Computing10.1109/TSUSC.2018.28784387:2(313-323)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TSUSC.2018.2878438
Eanes R(2022)Cell Phone DataEncyclopedia of Big Data10.1007/978-3-319-32010-6_33(152-155)Online publication date: 12-Feb-2022
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Walelgne EAsrese AManner JBajpai VOtt J(2021)Understanding Data Usage Patterns of Geographically Diverse Mobile UsersIEEE Transactions on Network and Service Management10.1109/TNSM.2020.303750318:3(3798-3812)Online publication date: Sep-2021
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Yin JFan YXia TLi YChen XZhou ZChen X(2020)Mobile App Usage Patterns Aware Smart Data PricingIEEE Journal on Selected Areas in Communications10.1109/JSAC.2020.297189638:4(645-654)Online publication date: Apr-2020
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Wang XZhou ZXiao FXing KYang ZLiu YPeng C(2019)Spatio-Temporal Analysis and Prediction of Cellular Traffic in MetropolisIEEE Transactions on Mobile Computing10.1109/TMC.2018.287013518:9(2190-2202)Online publication date: 1-Sep-2019
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Shi HLi Y(2018)Discovering Periodic Patterns for Large Scale Mobile Traffic Data: Method and ApplicationsIEEE Transactions on Mobile Computing10.1109/TMC.2018.279994517:10(2266-2278)Online publication date: 1-Oct-2018
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