Performance Modeling of Multi-tiered Web Applications with Varying Service Demands

Ajay Kattepur, Manoj Nambiar


Multi-tiered transactional web applications are frequently used in IT enterprise based systems. Due to their inherent distributed nature, pre-deployment testing for high-availability and varying concurrency are important for post-deployment performance. Accurate performance modeling of such applications can help estimate values for future deployment variations as well as validate experimental results. In order to theoretically model performance of multi-tiered applications, we use queuing networks and Mean Value Analysis (MVA) models. While MVA has been shown to work well with closed queuing networks, there are particular limitations in cases where the service demands vary with concurrency. This variation of service demands for various resources (CPU, Disk, Network) is demonstrated through multiple experiments. This is further contrived by the use of multi-server queues in multi-core CPUs, that are not traditionally captured in MVA. We compare performance of a multi-server MVA model alongside actual performance testing measurements and demonstrate this deviation. Using spline interpolation of collected service demands, we show that a modified version of the MVA algorithm (called MVASD) that accepts an array of service demands, can provide superior estimates of maximum throughput and response time. Results are demonstrated over multi-tier vehicle insurance registration and e-commerce web applications. The mean deviations of predicted throughput and response time are shown to be less the 3% and 9%, respectively. Additionally, we analyze the effect of spline interpolation of service demands as a function of throughput on the prediction results. Using Chebyshev Nodes, the tradeoff between the number of test points and the spline interpolation/prediction accuracy is also studied. 


Performance Modeling; Queuing Networks; Mean Value Analysis; Multi-tier Applications; Spline Interpolation; Chebyshev Nodes

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