A Performance Study on the VM Startup Time in the Cloud

By Ming Mao, Marty A. Humphrey. 2012 IEEE Fifth International Conference on Cloud Computing, 2012

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Cloud auto-scaling with deadline and budget constraints (2010, methodology): “Though VMs can be acquired at any time, it may take some time for the instances to be ready to use (cloud VM startup delay [39][76]).”
A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments (2014, background): “Anticipation is important because there is always a delay from the time when an auto-scaling action is executed (for example, adding a server) until it is effective (for example, it takes several minutes to assign a physical server to deploy a VM, move the VM image to it, boot the operating system a”
A Survey on Cloud Computing Elasticity (2012, background): “…can vary (ranging from 1 to 10 minutes), depending on a number of factors including: type of cloud platform; operating system type; number, size, or speed of resources requested; the availability of spare resources in the requested region and the demand on the cloud platform from other users [48] .”
Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds (2014, methodology): “For example, Mao and Humphrey propose a dynamic approach for scheduling workflow ensembles on clouds [12].”