  We propose a simple yet effective policy for the predictive auto-scaling of
horizontally scalable applications running in cloud environments, where compute
resources can only be added with a delay, and where the deployment throughput
is limited. Our policy uses a probabilistic forecast of the workload to make
scaling decisions dependent on the risk aversion of the application owner. We
show in our experiments using real-world and synthetic data that this policy
compares favorably to mathematically more sophisticated approaches as well as
to simple benchmark policies.
