Scaling VM Deployment in an Open Source Cloud Stack

Interactive High Performance Computing (HPC) workloads take advantage of the elasticity of clouds to scale their com-putation based on user demand by dynamically provision-ing 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 tun-ing 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 implementa-tion, the deployment time improved by 12 × on average, to 53.54 seconds. These results suggest two possible improve-ment opportunities that can be useful for both cloud devel-opers 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 infrastruc-ture characteristics. Second, the code scalability issues can be avoided through a testing infrastructure that supports large scale emulation