Matrix: Achieving Predictable Virtual Machine Performance in the Clouds

Abstract

The success of cloud computing builds largely upon on-demand supply of virtual machines (VMs) that provide the abstraction of a physical machine on shared resources. Unfortunately, despite recent advances in virtualization technology, there still exists an unpredictable performance gap between the real and desired performance. The main contributing factors include contention to the shared physical resources among co-located VMs, limited control of VM allocation, as well as lack of knowledge on the performance of a specific VM out of tens of VM types offered by public cloud providers. In this work, we propose Matrix, a novel performance and resource management system that ensures the desired performance of an application achieved on a VM. To this end, Matrix utilizes machine learning methods clustering models with probability estimates to predict the performance of new workloads in a virtualized environment, choose a suitable VM type, and dynamically adjust the resource configuration of a virtual machine on the fly. The evaluations on a private cloud, and two public clouds (Rackspace and Amazon EC2) show that for an extensive set of cloud applications, Matrix is able to estimate application performance with average 90% accuracy. In addition, Matrix can deliver the target performance within 3% variance, and do so with the best cost-efficiency in most cases.