A Web Performance Modeling Process Based on the Methodology of Learning from Data

Accurate performance metric models are the key to Web capacity planning related problems. Due to the complexity of Web systems, analytical modeling without integrating the performance testing process is not enough to get accurate metric models. To integrate performance testing and analytical modeling in a systematic way, a Web performance modeling process is presented based on the methodology of learning from data. The process divides the modeling activity into several phases: constructing models and hypothetical conditions, deriving test cases, estimating parameters and validating models, etc. The scalability of a real Web community system (www.igroot.com) is studied by using the proposed process. The error of estimated saturation point is within 1 percent, the error of estimated lower bound of buckle point is within 5 percent. At last, a HTTP processing bottleneck at the architecture level is identified by correlating the model with the threads data of the Web server.