Linear parameter-varying model identification with structure selection for autonomic web service systems

Information technology (IT) systems must provide their users with prescribed quality of service (Qos) levels, usually defined in terms of application performance. Qos requirements are in general difficult to satisfy, since the system workload may vary by orders of magnitude within the same business day. To meet Qos requirements, resources have to dynamically be allocated among running applications, re-configuring them at run-time. To deal with resource allocation so as to manage system overload issues admission control and server virtualization are typically used: the former is a protection mechanism that rejects requests under peak workload, whereas the latter allows partitioning physical resources such as CPU and disks into multiple virtual ones. For designing effective controllers to ensure the desired Qos levels, a reliable model of the server dynamics is needed. The given systems, while retaining the time-varying nature that allows one to model workload variations. To address this issue, a constrained black-box subspace identification approach endowed with a novel structure selection is designed, and the performance of the identified models are assessed on experimental data