Using the Attribute-Driven Design for Automated Predictive Maintenance and Diagnostics of Complex Software Systems
April 2010 • Presentation
Aldo Dagnino discusses how analyzing trends in changes of key performance indicators (KPIs) can help in the architectural configuration of a large-scale system.
Software Engineering Institute
The objective of this presentation is to discuss how mining historical data that contains key performance indicators associated with the health of a large-scale system can help in its architecture configuration. By analyzing trends in changes in the key performance indicators (KPIs), knowledge about the health of the system can be obtained. This system health knowledge, used in conjunction with the principles of Attribute Driven Design (ADD), provides guidelines to make architectural changes into the configuration of a system. This presentation will outline how both the system health knowledge derived from data mining of KPIs and the ADD principles can contribute to finding new ways to configure the architecture of a system by paying special attention to key software qualities.