Summary Cloud computing is a system service model in which stakeholders deploy and run their software applications on a sophisticated infrastructure that is owned and managed by third-party providers. The ability of a given cloud infrastructure to effectively re- allocate resources to applications is referred to as elasticity. Of course, in practice, clouds are not perfectly elastic. Since cloud providers must provide elastic cloud services to a wide range of customers, their cloud platforms do not provision their resources precisely and automatically for specific applications. At the same time it is currently infeasible for cloud providers to allow customers to guide the cloud on how best to elastically provision their applications. To significantly reduce the cost of deploying software applications in the cloud, we solve a fundamental problem at the intersection of cloud computing and software performance testing. Our core idea is to automatically learn behavioral models of software applications during performance testing to synthesize provisioning strategies that are automatically tailored for these applications. With our idea, the problem of precise cloud elasticity is translated into enabling a feedback-directed loop between software development and cloud deployment. We implemented our approach and applied it to two software applications in the cloud environment, namely Cloudstack. Our experiments demonstrate that with our approach the cloud is able to provision resources more efficiently, so that the applications improve their throughput by up to over 40%.       Downloads and Experimental Results To reproduce results of our experiments with PRESTO, you need to obtain the following package and follow instructions. People PRESTO was created the College of William and Mary’s SEMERU group headed by Denys Poshyvanyk, at the Advanced Research In Software Engineering (ARISE) lab at the Department of Computer Science of the University of Illinois at Chicago where Mark Grechanik leads a research team, and by Adam Porter at the University of Maryland, College Park. Qi Luo, Project Lead Email: qluo[at]email.wm.edu Mark Grechanik, Project Co-Lead Email: drmark[at]uic.edu Denys Poshyvanyk Email: denys[at]cs.wm.edu Adam Porter  Email: aporter[at]cs.umd.edu