The design of flexible and efficient mechanisms for proper placement and chaining of virtual network functions (VNFs) is key for the success of Network Function Virtualization (NFV). Most state-of-the-art solutions, however, consider fixed (and immutable) flow processing and bandwidth requirements when placing VNFs in the Network Points of Presence (N-PoPs). This limitation becomes critical in NFV-enabled networks having highly dynamic flow behavior, and in which flow processing requirements and available N-PoP resources change constantly. To bridge this gap, we present NFV-PEAR, a framework for adaptive VNF placement and chaining. In NFV-PEAR, network operators may periodically (re)arrange previously determined placement and chaining of VNFs, with the goal of maintaining acceptable end-to-end flow performance despite fluctuations of flow processing costs and requirements. In parallel, NFV-PEAR seeks to minimize network changes (e.g., reallocation of VNFs or network flows). The results obtained from an analytical and experimental evaluation provide evidence that NFV-PEAR has potential to deliver more stable operation of network services, while significantly reducing the number of network changes required to ensure end-to-end flow performance.