Measuring service in multi-class networks

Quality of Service mechanisms and differentiated service classes are increasingly available in networks and servers. While network clients can assess their service by measuring basic performance parameters such as packet loss and delay, such measurements do not expose the network's core QoS functionality. In this paper, we develop a framework and methodology for enabling network clients to assess a system's multi-class mechanisms and parameters. Using hypothesis testing, maximum likelihood estimation, and empirical arrival and service rates measured across multiple time scales, we devise techniques for clients to (1) determine the most likely service discipline among EDF, WFQ, and SP, (2) estimate the server's parameters with high confidence, and (3) detect and parameterize non-work-conserving elements such as rate limiters. We describe the important role of time scales in such a framework and identify the conditions necessary for obtaining accurate and high confidence inferences.