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Emerging trends in Nodeless ecosystem

What is the problem that Nodeless is trying to solve?

Kubernetes capacity planning on public cloud is hard - average utilization of Kubernetes compute on public cloud is 20%. Cost of over and under provisioning compute capacity is high, and operational complexity of plethora of pet worker node options (on-demand, spot, CaaS) across various cloud providers is overwhelming.

What is Nodeless?

Instead of hand curating pet worker nodes, Nodeless enables you to manifest cloud compute for your pods in lock-step with pod lifecycle. Vanilla kubernetes bin packs large worker nodes with pods of varying sizes (resource requirements). Nodeless bin selects optimal bin for each pod. A pod's bin can be optimized along various axes: resource requirements, pod SLA, compute cost, cloud vendor preference, etc.

BinPacking vs BinSelection 

What is the catch?

Nodeless eco-system has the following big ticket items that are not yet supported (work in progress).

Persistent volumes: Pending implementation.

Daemon sets: Daemonsets come with the implication that you are bin packing nodes with pods. They represent products that were designed in the private datacenter era where you had large static server blades that needed one agent (for logging/monitoring/security/etc) per server blade.

In a nodeless world, since there is one compute launch type per pod, the intention of daemonset translates to the functionality of a sidecar. One way to execute the intent is to have an admission controller that converts daemonset to sidecar. The caveat of this approach is that some commercial offerings that are implemented via daemonset charge per node (ex: Datadog, Instana). Charging per node translates to charging per pod in a nodeless world which is cost prohibitive. Nodeless community aims to work with such daemonset-based products to arrive at a pod-based pricing model in a nodeless world.

Affinity and Anti-affinity rules: Similar to daemon sets, affinity and anti-affinity rules assume bin packed nodes. In a nodeless world, you get anti-affinity out of the box resulting in stronger multi-tenant security by design! Intent behind affinity rules can be manifested by having an admission controller that creates a single "nodeless pod" consisting of all the pods belonging to a single affinity rule.

What kind of workloads are best suited to run in a Nodeless environment?

CICD workloads like Jenkins and Buildkite jobs are best first fit for Nodeless. Since these workloads are time bound, bursty, stateless, and (relatively) low risk, they are ideally suited for trying out Nodeless.

What are my options for trying Nodeless?

Your best nodeless fit depends on your business and technical needs. If you do not have special needs for a bespoke stack wrt {CNI, CSI, security, logging, monitoring}, cloud provider options like EKS with Fargate on AWS and AKS with virtual nodes on Azure are a good start.

If you have technical and/or business needs that are unmet by bespoke stack (ex: custom networking/storage/security requirements), or, if you want to step away from cloud vendor lock in, nodeless setup using virtual-kubelet with a cloud vendor neutral provider is a better option.

How does Nodeless interop with my preferred {storage, networking, security, logging, monitoring} stack?

Nodeless interop with your custom stack depends on which Nodeless model you pick. For example, virtual-kubelet with Kloud Instance Provider gets you {CNI, Istio, logging, Prometheus} support out of the box, but is missing CSI support as of KubeConEU 2020.

Where can I learn more?

Blog Posts: https://medium.com/elotl-blog

KubeCon talk: Virtual Kubelet KubeCon 2019 talk

I have a Nodeless question. Where do I ask?

Please share your questions/comments in Nodeless community channel #virtual-kubelet in the Kubernetes slack!

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About the Author

Madhuri Yechuri, CEO, Elotl

Madhuri Yechuri 

Madhuri Yechuri is a systems engineer with 20 years experience in database server technologies (Oracle), virtualization (VMware), and container technologies (ClusterHQ) before founding Elotl. Madhuri received her Masters in Computer Science from Indiana University Bloomington, and Bachelors in Computer Science from Indian Institute of Technology Kharagpur. https://www.linkedin.com/in/madhuri-yechuri-4111461/

Published Thursday, March 05, 2020 9:41 AM by David Marshall
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