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SUMMARYThe efficient deployment of high performance computing applications on Clouds offers many challenges, in particular, for communication‐intensive applications. One strategy to mitigate performance overheads caused by high‐communication latency is to schedule requested virtual machines (VMs) effectively onto physical resources by optimizing VM placement. In current approaches based on Infrastructure as a Service resource provisioning, the task of selecting VM profiles is either left to the user or resolved with policies independent from usage patterns, with potential for performance degradation caused by virtualization overhead. Also, most VM placement heuristics disregard the topology of virtual clusters, resource usage patterns of each application, and competing workloads. In this paper, we study the case of scientific applications in virtual clusters by analyzing how different VM profiles and placements can affect observed performance of a parallel application that uses distributed memory. We propose a description for the characteristics of a virtual cluster through the placement of virtual cores, analyze different configurations with a software for systematic execution of virtual clusters, and explain the observed performance in terms of virtual cluster features and resource usage patterns of the application. Our analysis shows that other factors besides the number of cores can have significant effect on performance, such as virtual core mappings and VM spreading. We discuss how our methodology can be extended toward developing performance models, which are aware of resource contention and virtual cluster topology. Copyright © 2014 John Wiley & Sons, Ltd.