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