Modeling virtualized applications using machine learning techniques

With the growing adoption of virtualized datacenters and cloud hosting services, the allocation and sizing of resources such as CPU, memory, and I/O bandwidth for virtual machines (VMs) is becoming increasingly important. Accurate performance modeling of an application would help users in better VM sizing, thus reducing costs. It can also benefit cloud service providers who can offer a new charging model based on the VMs’ performance instead of their configured sizes. In this paper, we present techniques to model the performance of a VM-hosted application as a function of the resources allocated to the VM and the resource contention it experiences. To address this multi-dimensional modeling problem, we propose and refine the use of two machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). We evaluate these modeling techniques using five virtualized applications from the RUBiS and Filebench suite of benchmarks and demonstrate that their m...

• Type: Journal
• Year: 2012
• Where: VEE
• Authors: Sajib Kundu, Raju Rangaswami, Ajay Gulati, Ming Zhao, Kaushik Dutta
• Keywords: Resource Contention, Support Vector Machine, T Cloud, VEE 2012, Virtualization