提出了一种面向性能剖析的Web应用自动性能建模方法.该方法考虑了用户行为与系统中不同服务之间的关联,动态地构造与应用实际状态相符的性能模型,并利用Kalman滤波所具备的过滤“噪声”和适应变化的特性,精确估算各服务所需CPU时间.实验结果表明,该方法可以适应Web应用内、外部环境的变化,分析结果可为瓶颈定位和容量规划等性能保障技术提供高质量数据.
This paper proposes an automatic performance modeling approach for performance profiling of Web applications. In addition, the study proposes an automatic approach to build performance model. Both the user behaviors and their corresponding internal service relations are modeled, and the CPU time consumed by each service is also obtained through Kalman filter, which can "absorb" some level of noise in real-world data. Experimental results show that this approach can adapt to the change in both the inner and outer environments of Web applications and provide valuable information for capacity planning and bottleneck detection.