Performance measurement and analytic modeling of a web application

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.

Performance measurement and analytic modeling of a web application

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.

Personalized Decision Making on Genomic Testing in Early Breast Cancer: Expanding the MINDACT Trial with Decision-Analytic Modeling

Background Genomic tests may improve upon clinical risk estimation with traditional prognostic factors. We aimed to explore how evidence on the prognostic strength of a genomic signature (clinical validity) can contribute to individualized decision making on starting chemotherapy for women with breast cancer (clinical utility). Methods The MINDACT trial was a randomized trial that enrolled 6693 women with early-stage breast cancer. A 70-gene signature (Mammaprint) was used to estimate genomic risk, and clinical risk was estimated by a dichotomized version of the Adjuvant!Online risk calculator. Women with discordant risk results were randomized to the use of chemotherapy. We simulated the full risk distribution of these women and estimated individual benefit, assuming a constant relative effect of chemotherapy. Results The trial showed a prognostic effect of the genomic signature (adjusted hazard ratio 2.4). A decision-analytic modeling approach identified far fewer women as candidates for genetic testing (4% rather than 50%) and fewer benefiting from chemotherapy (3% rather than 27%) as compared with the MINDACT trial report. The selection of women benefitting from genetic testing and chemotherapy depended strongly on the required benefit from treatment and the assumed therapeutic effect of chemotherapy. Conclusions A high-quality pragmatic trial was insufficient to directly inform clinical practice on the utility of a genomic test for individual women. The indication for genomic testing may be far more limited than suggested by the MINDACT trial.