A factor analytic approach to inferring congestion sharing based on flow level measurements
Internet traffic primarily consists of packets from elastic flows, i.e., Web transfers, file transfers, and e-mail, whose transmissions are mediated via the Transmission Control Protocol (TCP). In this paper, we develop a methodology to process TCP flow measurements in order to analyze throughput correlations among TCP flow classes that can be used to infer congestion sharing in the Internet. The primary contributions of this paper are: 1) development of a technique for processing flow records suitable for inferring congested resource sharing; 2) evaluation of the use of factor analysis on processed flow records to explore which TCP flow classes might share congested resources; and 3) validation of our inference methodology using bootstrap methods and nonintrusive, flow level measurements collected at a single network site. Our proposal for using flow level measurements to infer congestion sharing differs significantly from previous research that has employed packet level measurements for making inferences. Possible applications of our method include network monitoring and root cause analysis of poor performance.