Large scale enterprise applications are known to observe dynamic workload; provisioning correct capacity for these applications remains an important and challenging problem. Predicting high variability fluctuations in workload or the peak workload is difficult; erroneous predictions often lead to under-utilized systems or in some situations cause temporarily outage of an otherwise well provisioned web-site. Consequently, rather than provisioning server capacity to handle infrequent peak workloads, an alternate approach of dynamically provisioning capacity on-the-fly in response to workload fluctuations has become popular. Cloud platforms are particularly suited for such applications due to their ability to provision capacity when needed and charge for usage on pay-per-use basis. Cloud environments enable elastic provisioning by providing a variety of hardware configurations as well as mechanisms to add or remove server capacity. The first part of this thesis presents Kingfisher, a cost-aware system that provides a generalized provisioning framework for supporting elasticity in the cloud by (i) leveraging multiple mechanisms to reduce the time to transition to new configurations, and (ii) optimizing the selection of a virtual server configuration that minimize cost. Majority of these enterprise applications, deployed as web applications, are distributed or replicated with a multi-tier architecture. SLAs for such applications are often expressed as a high percentile of a performance metric, for e.g. 99 percentile of end to end response time is less than 1 sec. In the second part of this thesis I present a model driven technique which provisions a multi-tier application for such an SLA and is targeted for cloud platforms. Enterprises critically depend on these applications and often own large IT infrastructure to support the regular operation of these applications. However, provisioning for a peak load or for high percentile of response time could be prohibitively expensive. Thus there is a need of hybrid cloud model, where the enterprise uses its own private resources for the majority of its computing, but then "bursts" into the cloud when local resources are insufficient. I discuss a new system, namely Seagull, which performs dynamic provisioning over a hybrid cloud model by enabling cloud bursting. Finally, I describe a methodology to model the configuration patterns (i.e deployment topologies) of different control plane services of a cloud management system itself. I present a generic methodology, based on empirical profiling, which provides initial deployment configuration of a control plane service and also a mechanism which iteratively adjusts the configuration to avoid violation of control plane's Service Level Objective (SLO).