Abstract. Supervisory control is the main means to assure a high level performance and availability of large IT infrastructures. Applied control theory is used in physical and virtualization based clustering, autonomic-, self-healing and cloud computing, but similar problems arise in any distributed environment. The selection of a compact, but sufficiently characteristic set of control variables is one of the core problems both for design and run-time complexity. Most results in the literature are based on a single algorithm for variable selection, but our measurements indicate that no single algorithm can generate faithful estimates for all the different operational domains. We propose to use a combination of different model extraction techniques on benchmark-like data logs. The main advantages of this multi-paradigm approach are twofold: it provides good parameter estimators for predictive control in a simple way; and supports the identification of the actual operational domain facilitating context-aware adaptive control, diagnostics and repair.

Performability control of IT systems still lacks theoretically well-founded approaches that fit well to enterprise system management solutions. We propose a methodology for designing compact qualitative, statebased predictive performability control that use instrumentation provided by typical system monitoring frameworks. We identify the main systemic insufficiencies of current monitoring tools that hinder designing trustworthy fine-granular controls.