ABSTRACT:
The execution of modern database applications requires the co-ordination of number of components such as: the application itself, the DBMS, the operating system, the network and the platform. The interaction of these components makes understanding the overall behavior of the application a complex task. As a result the effectiveness of optimizations are often difficult to predict. Three techniques commonly available to analyze system behavior are empirical measurement, simulation-based analysis and analytical modeling. To verify the effectiveness of the model a validation framework is developed. Database workloads are executed on the flexible Flask architecture on different platforms. Flask is designed to minimize the dependencies between DBMS components and is used in the framework to allow the same workloads to be executed on a various recovery mechanisms. Empirical analysis of executing the workloads is used to validate the assumptions about CPU, I/O and workload that underly MaStA. Once validated, the utility of the model is illustrated by using it to select the mechanisms that provide optimum performance for given database applications.