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 high-level latency SLO, and the performance of a low-level average CPU-based SLO, implemented by the Kubernetes Horizontal Pod Autoscaler.
Bartelucci, N., Bellavista, P., Pusztai, T., Morichetta, A., Dustdar, S. (2022). High-Level Metrics for Service Level Objective-aware Autoscaling in Polaris: a Performance Evaluation. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA : IEEE COMPUTER SOC [10.1109/ICFEC54809.2022.00017].
High-Level Metrics for Service Level Objective-aware Autoscaling in Polaris: a Performance Evaluation
Bartelucci, N;Bellavista, P;
2022
Abstract
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 high-level latency SLO, and the performance of a low-level average CPU-based SLO, implemented by the Kubernetes Horizontal Pod Autoscaler.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
