Architecture-based self-adaptation in the presence of multiple objectives

In the world of autonomic computing, the ultimate aim is to automate human tasks in system management to achieve high-level stakeholder objectives. One common approach is to capture and represent human expertise in a form executable by a computer. Techniques to capture such expertise in programs, scripts, or rule sets are effective to an extent. However, they are often incapable of expressing the necessary adaptation expertise and emulating the subtleties of trade-offs in high-level decision making. In this paper, we propose a new language of adaptation that is sufficiently expressive to capture the subtleties of choice, deriving its ontology from system administration tasks and its underlying formalism from utility theory.