ABSTRACT
Network traffic load in an IEEE802.11 infrastructure arises from the superposition of traffic accessed by wireless clients associated with access points (APs). An accurate characterization of these data can be beneficial in modelling network traffic and addressing a variety of problems including coverage planning, resource reservation and network monitoring for anomaly detection. This study focuses on the statistical analysis of the traffic load measured in a campus-wide IEEE802.11 infrastructure at each AP.
Using the Singular Spectrum Analysis approach, we found that the time-series of traffic load at a given AP has a small intrinsic dimension. In particular, these time-series can be accurately modelled using a small number of leading (principal) components. This proved to be critical for understanding the main features of the components forming the network traffic.
The statistical analysis of leading components has demonstrated that even a few first components form the main part of the information. The residual components capture the small irregular variations, which do not fit in the basic part of the network traffic and can be interpreted as a stochastic noise. Based on these properties, we also studied contributions of the various components to the overall structure of the traffic load of an AP and its variation over time.
- T. Henderson, D. Kotz, and I. Abyzov, "The changing usage of a mature campuswide wireless network", In ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom), Philadelphia, Sep. 2004. Google ScholarDigital Library
- M. Ploumidis, M. Papadopouli, and T. Karagiannis, "Multi-level application-based traffic characterization in a large-scale wireless network", in Proc. of the IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Helsinki, Finland, June 2007.Google ScholarCross Ref
- F. Anjum, M. Elaoud, D. Famolari, A. Ghosh, R. Vaidyanathan, A. Dutta, P. Agrawa, T. Kodama, and Y. Katsube, "Voice performance in WLan networks, an experimental study", in Proc. of the IEEE Conference on Global Communications (GLOBECOM), Rio De Janeiro, Brazil, Dec. 2003.Google ScholarCross Ref
- W. E. Leland, M. S. Taqqu, W. Willinger, and D. V. Wilson, "On the self-similar nature of ethernet traffic", ACM Computer Communication Review, 25(1):202--213, 1995. Google ScholarDigital Library
- W. E. Leland, W. Willinger, M. S. Taqqu, and D. V. Wilson, "Statistical analysis and stochastic modeling of self-similar datatraffic", in Proc. 14th Int. Teletraffic Cong., Vol. 1, pp 319--328, Antibes Juan Les Pins, France, June 1994.Google ScholarCross Ref
- A. Lakhina, K. Papagiannaki, M. Crovella, C. Diot, E. D. Kolaczyk, and N. Taft, "Structural Analysis of Network Traffic Flows", ACM Sigmetrics, New York, June 2004. Google ScholarDigital Library
- F. H. Campos, M. Karaliopoulos, M. Papadopouli, and H. Shen, "Spatio-Temporal Modeling of Traffic Workload in a Campus WLAN", 2nd annual intl. WIreless internet CONference (WICON'06), Boston, USA, August 2-5,2006. Google ScholarDigital Library
- M. Karaliopoulos, M. Papadopouli, E. Raftopoulos, and H. Shen, "On scalable measurement-driven modelling of traffic demand in large WLans", in Proc. of the IEEE Workshop on Local and Metropolitan Area Networks, Princeton NJ, USA, June 10-13, 2007.Google Scholar
- M. Papadopouli, H. Shen, E. Raftopoulos, M. Ploumidis, and F. Hernandez-Campos, "Short-term traffic forecasting in a campus-wide wireless network", 16th Annual IEEE Intl. Symp. on Personal Indoor and Mobile Radio Comm., Berlin, Germany, September 11-14, 2005.Google ScholarCross Ref
- M. Papadopouli, E. Raftopoulos, and H. Shen, "Evaluation of short-term traffic forecasting algorithms in wireless networks", 2nd Conf. on Next Generation Internet Design and Engineering, Valencia, Spain, April 3-5, 2006.Google ScholarCross Ref
- America's most connected campuses. http://forbes.com/home/lists/2004/10/20/04conncampland.html.Google Scholar
- H. D. I. Abarbanel, "Analysis of Observed Chaotic Data", Springer-Verlag New York, Inc., 1996.Google Scholar
- I. T. Jolliffe, "Principal Component Analysis", Springer-Verlag, 1986.Google Scholar
- N. Golyandina, V. Nekrutkin, and A. Zhigljavsky, "Analysis of Time Series Structure: SSA and Related Techniques", Chapman & Hall/CRC, 2001.Google Scholar
- I. Antoniou, V. V. Ivanov, Valery V. Ivanov, and P. V. Zrelov, "Principal Component Analysis of Network Traffic Measurements: the "Caterpillar"-SSA approach", VIII Int. Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT'2002, 24-28 June 2002, Moscow Russia.Google Scholar
- M. H. Hayes, "Statistical Digital Signal Processing and Modeling", John Wiley & Sons, 1996. Google ScholarDigital Library
- P. E. Greenwood, and M. S. Nikulin, "A Guide to Chi-Squared Testing", John Wiley & Sons Canada, Ltd., 1996.Google Scholar
- G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, "Time Series Analysis, Forecasting and Control", 3rd ed. Prentice Hall, Englewood Cliffs, NJ, 1994. Google ScholarDigital Library
- UNC/FO.R.T.H. archive of wireless traces, models and tools. http://netserver.ics.forth.gr/datatraces/Google Scholar
Index Terms
- Singular spectrum analysis of traffic workload in a large-scale wireless lan
Recommendations
Trend forecasting based on Singular Spectrum Analysis of traffic workload in a large-scale wireless LAN
Network traffic load in an IEEE802.11 infrastructure arises from the superposition of traffic accessed by wireless clients associated with access points (APs). An accurate load characterization can be beneficial in modeling network traffic and ...
Traffic differentiation and QoS provisioning for IEEE 802.11e wireless LAN
In 802.11e wireless local area network WLAN, MAC protocol is very much essential for offering multimedia quality of service QoS to WLAN users in terms of metrics like throughput, data drop, fairness and medium access delay. In addition, MAC protocol ...
Adaptive AIFS in wireless LAN
Network control and engineering for Qos, security and mobility IIThe new medium access protocol proposed by the IEEE 802.11e group, namely EDCF (Enhanced Distributed Coordination Function) provides a best way to guarantee a Qos for real time and multimedia applications. However the parameters used in EDCF are static ...
Comments