A method to compare scaling algorithms

Nowadays, many services are offered via the cloud, i.e., they rely on interacting software components that can run on a set of connected Commercial Off-The-Shelf (COTS) servers sitting in data centers. As the demand for any particular service evolves over time, the computational resources associated with the service must be scaled accordingly while keeping the Key Performance Indicators (KPIs) associated with the service under control. Consequently, scaling always involves a delicate trade-off between using the cloud resources and complying with the KPIs. In this paper, we show that a (workload-dependent) Pareto front embodies this trade-off's limits. We identify this Pareto front for various workloads and assess the ability of several scaling algorithms to approach that Pareto front.