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Quality-of-service mechanisms and differentiated service classes are increasingly available in networks and Web servers. While network and Web server clients can assess their service by measuring basic performance parameters such as packet loss and delay, such measurements do not expose the system's core QoS functionality such as multiclass service discipline. In this paper, we develop a framework and methodology for enabling network and Web server clients to assess system's multiclass 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 earliest deadline first, class-based weighted fair queuing, and strict priority; 2) estimate the system'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.