1. Self-adaptive autoscaling algorithm for SLA-sensitive applications running on the Kubernetes clusters.
- Author
-
Pozdniakova, Olesia, Cholomskis, Aurimas, and Mažeika, Dalius
- Subjects
- *
COOLDOWN , *ALGORITHMS , *VELOCITY , *SELF-adaptive software - Abstract
Most existing autoscaling approaches help to avoid violating the performance-related Service Level Objectives (SLO). However, these solutions do not aim to recover the SLO. The proposed novel autoscaling solution covers both SLO violation avoidance and recovery. The SLO failure avoidance part of the solution aims to avoid SLO violations by adjusting autoscaling thresholds based on compliance with SLO. It also dynamically selects the required CPU threshold, cooldown intervals and the number of replicas based on load velocity. The recovery part of the solution aims to recover SLO by additional resource provisioning if SLO is violated due to a delay or resource underestimation. The proposed implementation of the solution targets embarrassingly parallel workloads. It is compared with two autoscaling solutions in five workload scenarios, evaluating the ability of the solution to operate close to the defined SLO values. The results show that the proposed solution provides sufficient resources to support or recover performance-based SLOs. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF