Instance Type Selection in Proactive Horizontal Auto-Scaling
Fábio Morais, Raquel Borges de Freitas Lopes, Francisco Brasileiro · 2016
Horizontally scalable applications can potentially run very efficiently over IaaS environments. For that, application providers need to appropriately plan the resource capacity that is to be acquired from the cloud providers, such that, at any point in time, they allocate the smallest infrastructure that is needed to provide the required quality of service for their applications. Since the workload of these applications typically vary widely over time, proactive auto-scaling of the infrastructure is a must. In this paper, we study the impact that an efficient instance type selection based on demands of multidimensional resources has on the performance of proactive auto-scaling. This issue has been mostly overlooked in the related literature. Our results show that suitable selection of the most cost-effective instance type yields a potential cost saving of as much as 50% when compared to the case where the auto-scaling mechanism is oblivious to the instance type selection. We also show evidences that a large portion of applications can benefit of this selection. Finally, we propose a simple selection mechanism that can lead to reasonable cost savings at the expenses of a small number of SLO violations.