A simple and effective predictive resource scaling heuristic for large-scale cloud applications
Valentín Flunkert, Quentin Rebjock, Joel Castellon, Laurent Callot, Tim Januschowski · arXiv (Cornell University) · 2020
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.