An RLS Memory-based Mechanism for the Automatic Adaptation of VMs on Cloud Environments

Abstract

One key factor for Cloud computing success is the resource flexibility it provides. Because of this characteristic, academia and industry have focused their efforts on making efficient use of cloud computational resources without having to sacrifice performance. One way to achieve this purpose is through the automatic adaptation of the computational capabilities of VMs according to their resource utilization and performance. In this paper we present the design and preliminary results of our resource adaptation solution, which proactively adapts VMs (memory-based vertical scaling) to maintain an expected performance. Our solution targets multi-tier applications deployed on Cloud environments, and its core resides in RLS-based resource and performance predictors. Our results show that our solution, when compared with VMs with larger and permanently allocated computational resources, is able to maintain expected performance while reducing resource waste.