Vertical Pod Autoscaler (VPA) is one of Kubernetes’ most promising tools for optimizing resource usage, but its adoption remains surprisingly low—less than 1%! Why? Because while it’s great on paper, in practice, it comes with a host of limitations: disruptive pod restarts, conflicts with HPA, and a lack of control over when updates are applied.
In my latest blog post, I explore:
- Why VPA struggles to gain traction.
- The critical limitations holding it back (like pod eviction chaos and OOM roulette).
- Emerging solutions like in-place resizing and tools like Goldilocks.
- A roadmap for improving VPA, including better update control, advanced scheduling, and applying recommendations beyond pods to higher-level objects like Deployments and StatefulSets.
Whether you’re a Kubernetes enthusiast or someone who’s tangled with autoscaling nightmares, this post dives into the tech, the challenges, and some exciting ideas to make VPA shine.
In my latest blog post, I explore: - Why VPA struggles to gain traction. - The critical limitations holding it back (like pod eviction chaos and OOM roulette). - Emerging solutions like in-place resizing and tools like Goldilocks. - A roadmap for improving VPA, including better update control, advanced scheduling, and applying recommendations beyond pods to higher-level objects like Deployments and StatefulSets.
Whether you’re a Kubernetes enthusiast or someone who’s tangled with autoscaling nightmares, this post dives into the tech, the challenges, and some exciting ideas to make VPA shine.
Check it out here