KubeCon + CloudNativeCon North America 2024

A standout feature of Kubernetes is its sophisticated mechanism for pulling container images from repositories, aligning containers with the appropriate pods, and strategically deploying pods to nodes that meet their resource requirements—such as CPU, GPU, RAM, network, and storage. This process adheres to the defined affinity and anti-affinity specifications between pods and nodes. Despite these capabilities, the challenge of optimally arranging a multitude of workloads, each comprising several pods within a cluster, remains an ongoing endeavor. In our research, we illustrate that a set of YAML files, which detail a workload deployment request, can be systematically transformed into a Binary Integer Linear Programming (BILP) model. Depending on the specific optimization goals, the objective functions of the model can be tailored accordingly. With the imposition of broad conditions, it is feasible to derive an optimal solution that adheres to polynomial time complexity constraints.