Abstract
The performance characteristics such as throughput, resource utilization and response time of a system can be determined through measurement, simulation modeling and analytic modeling. In this thesis, measurement and analytic modeling approaches are applied to study the performance of a Apache-PHP-PostgreSQL web application. Layered Queueing Network (LQN) analytic modeling has been used to represent the system's performance model. The measurements found from load testing are compared with model analysis results for model validation. This thesis aims to show that LQN performance models are versatile enough to allow development of highly granular and easily modifiable models of PHP-based web applications and furthermore are capable of performance prediction with sufficiently high accuracy. Lastly, the thesis also describes utilities and methods used for load testing and determination of service demand parameters in our research work which would aid in shortening time required in development and study of performance models of similar systems.
Highlights
1.1 MotivationInternet-users commonly interact with websites, many of which are dynamic in nature
This chapter introduces MyBikeRoutes-OSM, a web application whose performance has been analyzed in this work using Layered Queueing Networks (LQN) models
Interested readers may further refer to the Software Performance Engineering (SPE) Process Flow Diagram [54], from which any performance study derives and which serves as the basis for Figure 8
Summary
Internet-users commonly interact with websites, many of which are dynamic in nature. These sites generate content to suit user requests instead of only serving static web pages. Due to the functionality and interactivity provided by these dynamic websites they are more appropriately considered as Web Applications [23]. If only functional characteristics are considered web applications will seriously suffer in performance. Software Performance Engineering (SPE), which is a well-defined systematic and quantitative process integrates performance considerations in the SDLC, relying on a proactive rather than a reactive approach, where the former keeps track of system performance and mitigates problems before they happen, whereas latter is a restricted response once problems are encountered [25]
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