Enhancing Rules For Cloud Resource Provisioning Via Learned Software Performance Models
Mark Grechanik, Qi Luo, Denys Poshyvanyk, Adam Porter · 2016
In cloud computing, 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. To enable elasticity, programmers study the behavior of applications and write scripts that guide the cloud to provision resources for these applications. This is an imprecise, laborious, manual and expensive approach that drastically increases the cost of application deployment and maintenance in the cloud. We propose an approach, coined as Provisioning Resources with Experimental SofTware mOdeling (PRESTO), to automatically learn behavioral models of software applications during performance testing in order to recommend programmers how to improve provisioning strategies that guide the cloud to (de)allocate resources to these applications. We applied PRESTO to two software applications and our experiments demonstrate that with PRESTO programmers can create rules for provisioning resources with a high degree of precision when the performance is about to worsen, so that the applications maintain their throughputs at the desired level.