Control Theory, Controllers and Kubernetes: The Holy Trilogy

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Control Theory, Controllers and Kubernetes: The Holy Trilogy Madhav Jivrajani, VMware

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$ whoami Hi! ● Currently a senior @ PES University, Bangalore, India ● Been with the Kubernetes community for ~ 1 year ● Work on upstream Kubernetes @ VMware ● SIG-{ContribEx, API-Machinery, Node, Architecture}

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Outline ● Containerized workloads and orchestration ● Can we build systems that can self-assess and self-heal? ● Control Theory and PID Control ● Where does Kubernetes come in? ● Extending Kubernetes ● Demo ● How does CAPI use CRs?

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Containerized Workloads and Orchestration

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What happens if something goes wrong?

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How do we rectify this and who does it?

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What if we want to run another app?

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The answer to these questions is: orchestration!

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The question that aries now is: how does the orchestrator make decisions?

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The question that aries now is: how does the orchestrator make decisions? Does a sysadmin monitor the health of the applications and decide what actions to take?

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The question that aries now is: how does the orchestrator make decisions? Can the orchestrator be intelligent in some form and make these decisions, and if so, what information would it require?

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The question that aries now is: how does the orchestrator make decisions? Given the information needed, how can the orchestrator keep the system in a stable state?

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Let’s start with the Control Theory “Hello World” example

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A Few Terms ● The entity that we want to control - System ○ A system can take inputs and produce outputs. ● Where we want to go/our desired state/intent - Set Variable (SV) ● Where we currently are/observed state - Process Variable (PV) ○ System output ● How “far” are we currently from our desired state? - Error (e) ○ e = SP - PV ● Who drives the system to where it needs to be? - Controller ● A controller that provides system inputs based on system outputs - Closed Loop Controller

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However, it’s often not this ideal. With the controller applying adjustments, the following questions come to mind: ● What if the adjustment applied overshoots or undershoots the SV? ○ If it does, its likely to cause a thrashing effect. ● Can we take past experiences into account and adjust accordingly or in other words, can we compensate? ● Can we look at our current error and predict what the error is going to be in the future?

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Past, Present and Future - PID Controller

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Past, Present and Future - PID Controller ● P - Proportional: Adjust proportional to the error

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Past, Present and Future - PID Controller ● I - Integral: Adjust based on what the current error is and what the error has been in the past

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Past, Present and Future - PID Controller ● D - Derivative: Predict the future error based on rate of change of current error

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Can we look at our orchestrator as a control system?

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Is it possible for the orchestrator to self-assess and self-heal?

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Is it possible for the orchestrator to self-assess and self-heal? Yes.

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In order to do this, you need two pieces of information: 1. Where do you want to go? 2. Where are you currently?

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This largely shapes up to be a closed-loop controller.

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Where does Kubernetes ﬁt in as an orchestrator?

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Let’s dive a little deeper into how Kubernetes does things using these concepts.

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All Kubernetes controllers are P controllers!

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Kubernetes has a few build-in resources such as pod, deployments, replicasets, etc.

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Each of which are accessed through the Kubernetes API (which is a REST based API)

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kubectl get pods --namespace mynamespace GET /api/v1/namespaces/mynamespace/pods

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Other than the built-in resources, Kubernetes allows us to create custom resources. For ex: we can have a custom resource called foo and we can access it through kubectl like any other resource: kubectl get foo GET /apis/{group}/{version}/namespaces/{namespace}/foos

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Custom Resources are created from Custom Resources Deﬁnitions (CRDs). For custom resources created, we can also write custom controllers that have sufﬁcient intelligence baked in to reconcile any state changes.

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Demo https://github.com/kubernetes/sample-controller/

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This is extremely powerful for a multitude of reasons, because now you don’t have to look at Kubernetes as only a container orchestration platform.

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With CRs, Kubernetes can now be looked at as a “Universal Control Plane” or a “Platform For Building Platforms” ● With this extensibility, we can now create a custom resource that maybe represents an Infrastructure component, ex - VMs. ● We can also write custom controllers, similar to the built-in controllers that Kubernetes has, to try and reconcile state. ● With this, we can declaratively manage and provision infrastructure, by using Kubernetes as a base layer.

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Projects such as Cluster API (CAPI) and Crossplane make use of this extensibility in a similar manner to provision and/or manage infrastructure.

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For ex: CAPI deﬁnes a few CRDs, some of which are: ● Machine ○ Analogous to Kubernetes Pods ● MachineDeployment ○ Analogous to Kubernetes Deployment ● MachineSet ○ Analogous to Kubernetes ReplicaSet

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https://itnext.io/kubernetes-cluster-creation-on-baremetal-host-using-cluster-api-1c2373230a17

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References and Resources ● Control Theory In Container Fleet Management ● PID Loops and The Art of Keeping Systems Stable ● Kubernetes Design Principles - Understanding The Why ● The Magic of Kubernetes Self-Healing Capabilities ● Imperative, Declarative and Kubernetes ● Deep Dive into Kubernetes Internals for Builders and Operators ● The Cluster API Book

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Thank you! Twitter: @MadhavJivrajani GitHub: github.com/MadhavJivrajani K8s slack (slack.k8s.io): @madhav