Interactive High Performance Computing (HPC) workloads take advantage of the elasticity of clouds to scale their computation based on user demand by dynamically provisioning virtual machines during their runtime. As in this case users require the results of their computation in a short time, the time to start the provisioned virtual instances becomes crucial. In this paper we study the deployment scalability of OpenNebula, an open-source cloud stack, with respect to these workloads. We describe our efforts for tuning the infrastructure's and OpenNebula's configuration as well as solving scalability issues in its implementation. After tuning both infrastructure and cloud stack, the simultaneous deployment of 512 VMs improved by 5.9× on average, from 615 to 104 seconds, and after optimizing the implementation, the deployment time improved by 12× on average, to 53.54 seconds. These results suggest two possible improvement opportunities that can be useful for both cloud developers and scientific users deploying a cloud stack to avoid such scalability issues in the future. First, the tuning process of a cloud stack can be improved through automatic tools that adapt the configuration to the workload and infrastructure characteristics. Second, the code scalability issues can be avoided through a testing infrastructure that supports large scale emulation.