Autoscaling via Online Optimization With Switching Cost Constraints

In cloud services, autoscaling is one of the most important features, which enables system intelligence to adaptively assign computing resources for users according to real-time workloads and quality of service requirements. In our paper, we treat the process of autoscaling as a sequential decision problem of online optimization with switching cost constraints and propose two algorithms to solve it using noisy predictions for future workloads. Particularly, these two algorithms, which are called C-AFHC and SC-AFHC respectively, are designed for different situations according to a parameter of the constraints. Both of them have theoretical guarantees in terms of our well-defined performance metrics. Using real workload data collected from an enterprise Cloud Service, we demonstrate the performance of our algorithms in different scenarios of autoscaling problems.