Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
IEEE Transactions on Computers
A toolset for performance engineering and software design of client-server systems
Performance Evaluation - Special issue: performance modeling tools
Performance evaluation of software architectures
Proceedings of the 1st international workshop on Software and performance
Web server performance measurement and modeling techniques
Performance Evaluation - Special issue on tools for performance evaluation
Enterprise JavaBeans, Second Edition
Enterprise JavaBeans, Second Edition
Software Bottlenecking in Client-Server Systems and Rendezvous Networks
IEEE Transactions on Software Engineering
Software Performance Evaluation by Models
Performance Evaluation: Origins and Directions
An e-Business Integration & Collaboration Platform for B2B e-Commerce
WECWIS '01 Proceedings of the Third International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems (WECWIS '01)
Research on on-demand grid services access
Neural, Parallel & Scientific Computations - Special issue: Grid computing
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Abstract: 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.