Skip to Main Content
The complexity of information technology (IT) systems is steadily increasing. System complexity has been recognized as the main obstacle to the further advancement of IT and has recently raised energy management issues. Control techniques have been proposed and successfully applied to design autonomic computing systems, i.e., systems able to manage themselves trading-off system performance with energy reduction goals. As users' behavior is highly time varying and workload conditions can change substantially within the same business day, the linear parametrically varying (LPV) framework proves particularly suitable for modeling such systems. In this paper, the identification of single-input-single-output and multiple-input-multiple-output state space LPV models for the performance control of autonomic web service systems is addressed. Specifically, subspace LPV identification methods are shown to yield accurate dynamic models for the considered application. Their effectiveness is assessed on experimental data measured on a custom implementation of a workload generator and micro-benchmarking Web service applications.