HYPA: Hybrid Horizontal Pod Autoscaling with Automated Model Updates

Due to changing demand patterns driven by technological advancements and the rise of new applications and services, the provisioning of heterogeneous workloads is a crucial component of the resource allocation problem. Traditional resource allocation strategies such as reactive autoscaling or prediction-based proactive solutions, fail to meet the desired performance goals when the underlying demand arrival pattern changes. In this paper, we present HYPA, which combines reactive and proactive components to autoscale pods in a Kubernetes environment. In contrast to previous approaches of hybrid autoscaling, HYPA automatically reacts to drifts in the request arrival pattern. Specifically, it updates the model of its proactive component when the prediction performance decreases. The evaluation in a simulation on a variety of real-world traces, spanning multiple days, demonstrates that HYPA improves upon existing purely reactive and purely proactive horizontal pod autoscalers.