Summary:
This project aims to develop and evaluate a coherent set of methods to understand behavior in complex information systems, such as the Internet, computational grids and computing clouds. Such large distributed systems exhibit global behavior arising from independent decisions made by many simultaneous actors, which adapt their behavior based on local measurements of system state. Actor adaptations shift the global system state, influencing subsequent measurements, leading to further adaptations. This continuous cycle of measurement and adaptation drives a time-varying global behavior. For this reason, proposed changes in actor decision algorithms must be examined at large spatiotemporal scale in order to predict system behavior. This presents a challenging problem.
Measurement Science for Complex Information Systems
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