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.
@article{arxiv.2008.01215,
title = {A simple and effective predictive resource scaling heuristic for large-scale cloud applications},
author = {Valentin Flunkert and Quentin Rebjock and Joel Castellon and Laurent Callot and Tim Januschowski},
journal= {arXiv preprint arXiv:2008.01215},
year = {2020}
}