Groenda, HenningStier, Christian2023-03-132023-03-132015https://dl.gi.de/handle/20.500.12116/40769In Infrastructure as a Service (IaaS) Cloud scenarios, data center operators require specifications of Virtual Machine (VM) behavior for data center middle- and long-term planning and optimization. The planning is usually supported by simulations. While users can leverage white-box application knowledge, data center operators have to rely on metrics at the level of resource demands provided by virtualization and cloud middleware platforms. Existing simulations for data center planning do not combine both viewpoints and either require white-box knowledge or focus on short-term predictions using statistical estimators. Our approach allows modeling varying resource demand of black-box VMs based on the Descartes Load Intensity Model (DLIM). The black-box VM models are integrated in the SimuLizar performance simulator complementing the existing grey- and white-box models in order to improve reasoning on (de-) consolidation decisions.enPerformance PredictionModelingSimuLizarDLIMPalladioDesign-TimeText/Journal Article0720-8928