Cloud computing offers a paradigm shift in management of computing resources for large-scale applications. Using the Infrastructure-as-a-service (IaaS) cloud computing model, users today can request dynamically provisioned, virtualized resources such as CPU, memory, disk, and network access in the form of virtualized resources. The client typically requests resources based on computational needs and pays for resource instances based on their capacity and time utilized. Mapping these virtual resource requests to physical hardware could vary for identical requests. This can potentially cause variations in the performance of applications deployed on such resources. The performance of the application can vary according to the physical layout of the provisioned hardware (the number of virtual machines (VMs), the size/configuration of the VMs and the inter-VM locality). In this paper, we study the effects of this “provisioning variation” and its impact on application performance using suitable benchmarks as well as demonstrate their effect on a few MapReduce workloads. Our initial findings indicate that provisioning variation can impact performance by a factor of 5 primarily due to I/O contention.