AHP4HPA: An AHP-based Autoscaling Framework for Kubernetes Clusters at the Network Edge

Autoscaling resources in a power-efficient way is essential to enable Green Computing resource management solutions. The development of dynamic resource provisioning techniques could lead to the minimization of power consumption and simultaneously guarantee high quality of service (QoS) inline with the workload demand. In this work, we introduce AHP4HPA, an autoscaling framework for Kubernetes Clusters, which is aligned with the Kubernetes architecture and state-ofthe-art practices. We define resource profiles, namely a mapping between the QoS and the computing resources, to maximize the performance. Furthermore, Analytic Hierarchy Process (AHP) is exploited to dictate the scaling decision of the resources under various Key Performance Indicators (KPIs) toward power optimization of the allocated resources. To guarantee maximum performance of the deployed image classification application, an ARIMA model is dedicated to providing predictions regarding the incoming workload traffic. The framework is evaluated against a realistic dataset in a small-scale testbed. Numerical results indicate at least a 9% reduction of the average energy consumption when compared to other state of the art techniques.

Automated summary

This research introduces an autoscaling framework called AHP4HPA, which is aligned with the Kubernetes architecture and state-of-the-art practices. It aims to maximize performance and optimize power consumption by defining resource profiles and utilizing the Analytic Hierarchy Process (AHP).