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

机译：基于数据学习方法的Web性能建模过程

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摘要

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.
机译：准确的性能指标模型是与Web容量规划相关的问题的关键。由于Web系统的复杂性，没有集成性能测试过程的分析建模不足以获取准确的度量模型。为了以系统的方式集成性能测试和分析建模，基于从数据中学习的方法，提出了一个Web性能建模过程。该过程将建模活动分为几个阶段：构建模型和假设条件，导出测试用例，估计参数和验证模型等。通过使用所提出的过程，研究了真实Web社区系统（www.igroot.com）的可伸缩性。 。估计饱和点的误差在1％以内，弯曲点估计下限的误差在5％以内。最后，通过将模型与Web服务器的线程数据相关联来识别体系结构级别的HTTP处理瓶颈。

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