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Traditional capacity sizing of enterprise systems relies on benchmarking a system using benchmark clients that generate a workload pattern similar to real-world workload. When new functions are added to the system or when the workload pattern changes, benchmarking has to be performed again. This is a costly and time-consuming approach for capacity planning. Layered queueing models have been used to study the performance of software systems. The approach is able to identify major performance parameters of software systems. Given a workload pattern, the models can be solved analytically to predict the system performance quickly. This paper proposes a layered queueing model for predicting the performance of distributed enterprise applications built on Enterprise JavaBeans (EJB) technology. We show how such models can be applied for capacity sizing of distributed enterprise systems. We demonstrate this by using this methodology to predict the performance of a sample application built on an EJB-based business-to-business e-commerce platform. We compare deployment options and study the effect of different workload patterns on system capacity.
Liu et al. (Wed,) studied this question.