A Performance Study on the VM Startup Time in the Cloud

TLDR

Cloud elasticity permits dynamic scaling, yet prolonged VM startup times can cause under‑provisioning and degrade application performance. This study seeks to deepen understanding of VM startup latency so users can schedule resources proactively. We measured startup times on Amazon EC2, Windows Azure, and Rackspace, analyzing how time of day, OS image size, instance type, data center location, and concurrent instance count influence latency. Our results show that EC2 spot instances experience longer waits and greater variance than on‑demand instances.

Abstract

One of many advantages of the cloud is the elasticity, the ability to dynamically acquire or release computing resources in response to demand. However, this elasticity is only meaningful to the cloud users when the acquired Virtual Machines (VMs) can be provisioned in time and be ready to use within the user expectation. The long unexpected VM startup time could result in resource under-provisioning, which will inevitably hurt the application performance. A better understanding of the VM startup time is therefore needed to help cloud users to plan ahead and make in-time resource provisioning decisions. In this paper, we study the startup time of cloud VMs across three real-world cloud providers -- Amazon EC2, Windows Azure and Rackspace. We analyze the relationship between the VM startup time and different factors, such as time of the day, OS image size, instance type, data center location and the number of instances acquired at the same time. We also study the VM startup time of spot instances in EC2, which show a longer waiting time and greater variance compared to on-demand instances.