Paper Info
Title
Singular spectrum analysis of traffic workload in a large-scale wireless lan
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.
Year
DOI
Venue
2007
10.1145/1298126.1298146
MSWiM
Keywords
Field
DocType
modelling network traffic,small irregular variation,small intrinsic dimension,network traffic load,traffic load,large-scale wireless lan,network monitoring,statistical analysis,singular spectrum analysis,basic part,network traffic,traffic workload,small number,time series,anomaly detection,intrinsic dimension
Anomaly detection,Traffic generation model,Wireless,Computer science,Computer network,Intrinsic dimension,Singular spectrum analysis,Network monitoring,Network traffic simulation,Network traffic control,Distributed computing
Conference
Citations 
PageRank 
References 
7
0.61
6
Authors
3
Name
Order
Citations
PageRank
George Tzagkarakis113917.94
Maria Papadopouli252058.57
P. Tsakalides3954120.69