一种Web系统性能测试框架及其混合建模过程

A Web Performance Testing Framework and Its Mixed Performance Modeling Process

由于Web系统的复杂性,仅采用性能测试或单一建模方法在多个性能指标度量准确性、预测有效性和测试迭代控制等方面存在局限性.提出一种支持混合性能建模的Web性能测试框架,依据不同的性能指标,采取不同的性能建模方法,导出性能指标的封闭函数及其度量假设条件,执行回归分析和测试.以一个实际Web社区系统为例,针对系统响应时间和伸缩性指标,提出了排队网模型化简方法和伸缩性模型US-γ的混合建模与测试过程.测试结果表明,预测响应时间错误率在4%以内,预测吞吐量饱和点错误率在1%以内,预测拐点下界错误率在5%以内.通过关联系统与Web服务器线程2个伸缩性模型,在构架级识别出一个HTTP处理瓶颈.

Methods of pure performance testing or single analytical modeling, such as queueing network model, etc, have their limitation on the accuracy of performance indexes measurement, the validity of performance forecasting, and the controlling of testing iteration due to the complexity of Web systems. A Web performance modeling framework supporting mixed performance modeling is proposed. It uses different performance modeling methods for different kinds of performance indexes to derive closed form functions and their hypothesis of measurement. The regression analysis and testing are used on the training data to estimate the parameters of the closed form functions. To demonstrate the feasibility and validity of this framework, a real-world Web community system （igroot. corn） is studied under the framework. For the indexes of system response time and scalability, a mixed modeling method is proposed by combining queueing network reduction and extended universal scalability model US-γ. Compared with other practical system performance testing methods, such as universal scalability model US, the model accuracy of performance forecasting is greatly improved and the cost of software and hardware used in the process is greatly reduced. The error rate of estimated response time is within 4 percent, the error rate of estimated throughout saturation point is within 1 percent, and the error rate of estimated infimum of buckle point is within 5 percent. Correlating the scalability model and threads data of the Web server, an HTTP processing bottleneck at the architecture level is identified.