Benefits of Pod dimensioning with best-effort resources in bare metal cloud native deployments

Existing container orchestration platforms provide frameworks capable of automatically scaling resources to evolving traffic conditions based on resource usage. Service providers rely on these automatic scaling capabilities to improve the traffic adaptability of their cloud native applications while reducing costs. However, this may lead to service degradation related to the delayed response to the traffic changes. Traditionally, Pod dimensioning has been performed considering guaranteed (or request) resources. Recently, container orchestration platforms included the possibility to allow Pods to use idle resources that can be withheld for a short period of time in a best-effort fashion (known as limit resources). This paper analyzes the potential of limit resources as a way to mitigate degradation while reducing the amount of request resources. Results for a sample case show that a strategy relying on limit resources achieves the same level of degradation as a conventional strategy that uses only request resources, while reducing requested CPU by 25%.