[Zurück]

N. Bartelucci, P. Bellavista, T. Pusztai, A. Morichetta, S. Dustdar:
"High-Level Metrics for Service Level Objective-aware Autoscaling in Polaris: a Performance Evaluation";
Vortrag: 6th IEEE International Conference on Fog and Edge Computing (ICFEC 2022) - Hybrid Conference, Taormina, Italy; 18.05.2022 - 19.05.2022; in: "Proceedings of the 6th IEEE International Conference on Fog and Edge Computing (ICFEC 2022)", L. Mashayekhy, S. Schulte, V. Cardellini, B. Kantarci, Y. Simmhan, B. Varghese (Hrg.); IEEE, (2022), ISBN: 978-1-6654-9525-7; S. 73 - 77.

With the increasing complexity, requirements, and variability of cloud services, it is not always easy to find the right static/dynamic thresholds for the optimal configuration of low-level metrics for autoscaling resource management decisions. A Service Level Objective (SLO) is a high-level commitment to maintaining a specific state of a service in a given period, within a Service Level Agreement (SLA): the goal is to respect a given metric, like uptime or response time within given time or accuracy constraints. In this paper, we show the advantages and present the progress of an original SLO-aware autoscaler for the Polaris framework. In addition, the paper contributes to the literature in the field by proposing novel experimental results comparing the Polaris autoscaling performance, based on highlevel latency SLO, and the performance of a low-level average CPU-based SLO, implemented by the Kubernetes Horizontal Pod Autoscaler.

Cloud, Edge, Computing, Autoscaling, Polaris, Kubernetes, Performance, Evaluation, Horizontal, Pod, Autoscaler, Elasticity, High-level, SLO, Horizontal Pod Autoscaler

http://dx.doi.org/10.1109/ICFEC54809.2022.00017