Kubernetes is great for self-healing, and has a variety of ways to address initial placement to distribute workloads (policies and factors that the scheduler will take into account) along with ways to react to node pressure conditions (evictions and manual intervention). Today we will talk about a preventative approach, that is continuously analyzing actual utilization, changes in demand, and takes constraints into account to mitigate risks of performance issues without having to over-provision. We will deep dive into the analytics model, how these decisions are made, and how the action of what pod to move when and where is orchestrated based on native Kubernetes capabilities, such as VM Live Migration.
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