Article

Singular spectrum analysis of traffic workload in a large-scale wireless lan

Authors:

George Tzagkarakis,

Maria Papadopouli,

Panagiotis TsakalidesAuthors Info & Claims

MSWiM '07: Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems

Pages 99 - 108

https://doi.org/10.1145/1298126.1298146

Published: 23 October 2007 Publication History

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.

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Index Terms

Singular spectrum analysis of traffic workload in a large-scale wireless lan
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
2. Networks
  1. Network services
    1. Network monitoring

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Published In

cover image ACM Conferences

MSWiM '07: Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems

October 2007

422 pages

ISBN:9781595938510

DOI:10.1145/1298126

General Chair:
Carla-Fabiana Chiasserini
Politecnico di Torino, Italy
,
Program Chairs:
Nael Abu-Ghazaleh
State University of New York--Binghamton, NY
,
Sotiris Nikoletseas
University of Patras and CTI, Greece

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 23 October 2007

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MSWiM07

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MSWiM07: 10th International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems

October 22 - 26, 2007

Crete Island, Chania, Greece

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Overall Acceptance Rate 398 of 1,577 submissions, 25%

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Cited By

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https://doi.org/10.1007/978-3-031-48930-3_17
Zhu YRuffing NGurary JGuan YBettati R(2019)Towards Smartphone Operating System IdentificationIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2019.2892482(1-1)Online publication date: 2019
https://doi.org/10.1109/TDSC.2019.2892482
Duarte FRios RHruschka Ede Mello R(2019)Decomposing time series into deterministic and stochastic influencesDigital Signal Processing10.1016/j.dsp.2019.10258295:COnline publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1016/j.dsp.2019.102582
Ruffing NZhu YLibertini RGuan YBettati R(2016)Smartphone reconnaissance: Operating system identification2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC.2016.7444941(1086-1091)Online publication date: Jan-2016
https://doi.org/10.1109/CCNC.2016.7444941
Gurary JZhu YBettati RGuan Y(2016)Operating System FingerprintingDigital Fingerprinting10.1007/978-1-4939-6601-1_7(115-139)Online publication date: 26-Oct-2016
https://doi.org/10.1007/978-1-4939-6601-1_7
Guo YHuang SLi YNaik G(2013)Edge Effect Elimination in Single-Mixture Blind Source SeparationCircuits, Systems, and Signal Processing10.1007/s00034-013-9556-932:5(2317-2334)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1007/s00034-013-9556-9
Ma HJiang QLiu ZLiu GMa Z(2010)A novel blind source separation method for single-channel signalSignal Processing10.1016/j.sigpro.2010.05.02990:12(3232-3241)Online publication date: 1-Dec-2010
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Harris TYuan H(2010)Filtering and frequency interpretations of Singular Spectrum AnalysisPhysica D: Nonlinear Phenomena10.1016/j.physd.2010.07.005239:20-22(1958-1967)Online publication date: Oct-2010
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Tzagkarakis GPapadopouli MTsakalides P(2009)Trend forecasting based on Singular Spectrum Analysis of traffic workload in a large-scale wireless LANPerformance Evaluation10.1016/j.peva.2008.10.01066:3-5(173-190)Online publication date: 1-Mar-2009
https://dl.acm.org/doi/10.1016/j.peva.2008.10.010
Guyard FBeker S(2009)Towards real-time anomalies monitoring for QoE indicatorsannals of telecommunications - annales des télécommunications10.1007/s12243-009-0148-465:1-2(59-71)Online publication date: 19-Dec-2009
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