Distributed Resource Autoscaling in Kubernetes Edge Clusters

Maximizing the performance of modern applications requires timely resource management of the virtualized resources. However, proactively deploying resources for meeting specific application requirements subject to a dynamic workload profile of incoming requests is extremely challenging. To this end, the fundamental problems of task scheduling and resource autoscaling must be jointly addressed. This paper presents a scalable architecture compatible with the decentralized nature of Kubernetes [1], to solve both. Exploiting the stability guarantees of a novel AIMD-like task scheduling solution, we dynamically redirect the incoming requests towards the containerized application. To cope with dynamic workloads, a prediction mechanism allows us to estimate the number of incoming requests. Additionally, a Machine Learning-based (ML) Application Profiling Modeling is introduced to address the scaling, by co-designing the theoretically-computed service rates obtained from the AIMD algorithm with the current performance metrics. The proposed solution is compared with the state-of-the-art autoscaling techniques under a realistic dataset in a small edge infrastructure and the trade-off between resource utilization and QoS violations are analyzed. Our solution provides better resource utilization by reducing CPU cores by 8% with only an acceptable increase in QoS violations.