A Stratified Traffic Sampling Methodology for Seeing the Big Picture

This paper explores the use of statistical techniques, namely stratified sampling and cluster analysis, as powerful tools for deriving traffic properties at the flow level. The authors' results show that the adequate selection of samples leads to significant improvements allowing further important statistical analysis. Although stratified sampling is a well-known technique, the way they classify the data prior to sampling is innovative and deserves special attention. They evaluate two partitioning clustering methods, namely Clustering LARge Applications (CLARA) and K-means, and validate their outcomes by using them as thresholds for stratified sampling.