Multi-Application Hierarchical Autoscaling for Kubernetes Edge Clusters

The dynamic workload demands of smart city applications hosted on edge infrastructures require the development of advanced scaling mechanisms. Recent studies proposed single-application autoscaling solutions based on various technical approaches. However, for edge infrastructures with limited resource availability, it is essential to simultaneously manage heterogeneous application requirements, aiming at optimal resource allocation and minimal operational costs. This study introduces a multi-application hierarchical autoscaling framework for Kubernetes Edge Clusters. An application-based mechanism nominates the best applications’ deployments based on workload prediction and several criteria that guarantee the application’s performance while minimizing the infrastructure provider’s cost. For the joint application orchestration, an aggregation mechanism composes the candidate scaling solutions for the cluster. Then, a cluster autoscaling mechanism, based on the Analytic Hierarchy Process, undertakes the cluster’s scaling decision to optimize the resource allocation and energy consumption of the cluster. The evaluation illustrates the benefits of the proposed scaling strategy, achieving significant improvement in the average allocated resources and energy consumption compared to single-application approaches.