Modelling multi-tier enterprise applications behaviour with design of experiments technique

Queueing network models are commonly used for performance modelling. However, through application development stage analytical models might not be able to continuously reflect performance, for example due to performance bugs or minor changes in the application code that cannot be readily reflected in the queueing model. To cope with this problem, a measurement-based approach adopting Design of Experiments (DoE) technique is proposed. The applicability of the proposed method is demonstrated on a complex 3-tier e-commerce application that is difficult to model with queueing networks

Factors That Influence Throughput on Cloud-Hosted MySQL Server

Many businesses are moving their infrastructure to the cloud and may not fully understand the factors that can increase costs. With so many factors available to improve throughput in a database, it can be difficult for a database administrator to know which factors can provide the best efficiency to maintain lower costs. Grounded in Six Sigma theoretical framework, the purpose of this quantitative, quasi-experimental study was to evaluate the relationship between the time of day, the number of concurrent users, InnoDB buffer pool size, InnoDB Input/Output capacity, and MySQL transaction throughput to a MySQL database running on a cloud, virtual, database server. Data were collected from Debian Linux virtual machines (VMs) on Amazon Web Services, Google Cloud Platform, and Microsoft Azure using HammerDB database benchmarking software. The results of the one-way ANOVA were not significant. A key recommendation is to study further other factors and a more in-depth investigation into each cloud provider\u27s performance. The implications for positive social change include the potential for database administrators to make informed decisions on how to configure MySQL to run in a VM and choose the best cloud provider so that nonprofits may serve their clients more efficiently