Abstraction of physical hardware using infrastructure-as-a-service (IaaS) clouds leads to the simplistic view that resources are homogeneous and that infinite scaling is possible with linear increases in performance. Support for autonomic scaling of multi-tier service oriented applications requires determination of when, what, and where to scale. 'When' is addressed by hotspot detection schemes using techniques including performance modeling and time series analysis. 'What' relates to determining the quantity and size of new resources to provision. 'Where' involves identification of the best location(s) to provision new resources. In this paper we investigate primarily 'where' new infrastructure should be provisioned, and secondly 'what' the infrastructure should be. Dynamic scaling of infrastructure for service oriented applications requires rapid response to changes in demand to meet application quality-of-service requirements. We investigate the performance and resource cost implications of VM placement when dynamically scaling server infrastructure of service oriented applications . We evaluate dynamic scaling in the context of providing modeling-as-a-service for two environmental science models.