In multi-tenant cloud systems today, provisioning of resources for new tenancy is based on selection from a catalogue published by the cloud provider. The published images are generally a stack of appliances with Infrastructure (IaaS) and Platform (PaaS) layers and optionally Application layers (SaaS). Such a ready-made model enables quicker and streamlined resource provisioning to clients. However, this approach poses certain challenges to clients in the short run and providers in the long run. Unique tenancy requirements from each client are forcibly generalized by selecting one of the available images from the catalogue as the tenancy requirements are not modeled or validated to start with. Moreover, resource provisioning is mostly done towards addressing the peak load expectations in the tenancy. Such a static approach does not help in adapting to dynamically changing tenancy requirements, most often leading to the tenants owning and subsequently paying for more than what they need. In particular, provisioned resources are expected to perform at the same level of quality without accounting for their changing health. In our paper, we propose an extensible dynamic provisioning framework to address these challenges. We start with defining a Tenancy Requirements Model (TRM) which helps map provisioned resources with tenants. The provisioned and candidate resources are also modeled with their Quality of Service (QoS) characteristics which we call Health Grading Model (HGM); this helps in continuous monitoring and grading of resources based on health parameters and enables health prediction for future provisioning. Together, TRM and HGM allow dynamic re-provisioning for existing tenants based on either changing tenancy requirements or health grading predictions. We also present algorithms for prediction based provisioning and tenancy requirement matching. We illustrate our ideas throughout this paper with a running example, and present a proof-ofconcept prototype implementation on IBM's Rational Software Architect modeling tool. © 2012 IEEE.