Autoscaling the resources and services in your Kubernetes cluster is essential if your system is going to meet variable workloads. You can’t rely on manual scaling to help the cluster handle unexpected load changes.

While cluster autoscaling certainly allows for faster and more efficient deployment, the practice also reduces resource waste and helps decrease overall costs. When you can scale up or down quickly, your applications can be optimized for different workloads, making them more reliable. And a reliable system is always cheaper in the long run.

This tutorial introduces you to Kubernetes’s Cluster Autoscaler. You’ll learn how it differs from other types of autoscaling in Kubernetes, as well as how to implement Cluster Autoscaler using Rancher.

The differences between different types of Kubernetes autoscaling

By monitoring utilization and reacting to changes, Kubernetes autoscaling helps ensure that your applications and services are always running at their best. You can accomplish autoscaling through the use of a Vertical Pod Autoscaler (VPA), Horizontal Pod Autoscaler (HPA) or Cluster Autoscaler (CA).

VPA is a Kubernetes resource responsible for managing individual pods’ resource requests. It’s used to automatically adjust the resource requests and limits of individual pods, such as CPU and memory, to optimize resource utilization. VPA helps organizations maintain the performance of individual applications by scaling up or down based on usage patterns.

HPA is a Kubernetes resource that automatically scales the number of replicas of a particular application or service. HPA monitors the usage of the application or service and will scale the number of replicas up or down based on the usage levels. This helps organizations maintain the performance of their applications and services without the need for manual intervention.

CA is a Kubernetes resource used to automatically scale the number of nodes in the cluster based on the usage levels. This helps organizations maintain the performance of the cluster and optimize resource utilization.

The main difference between VPA, HPA and CA is that VPA and HPA are responsible for managing the resource requests of individual pods and services, while CA is responsible for managing the overall resources of the cluster. VPA and HPA are used to scale up or down based on the usage patterns of individual applications or services, while CA is used to scale the number of nodes in the cluster to maintain the performance of the overall cluster.

Now that you understand how CA differs from VPA and HPA, you’re ready to begin implementing cluster autoscaling in Kubernetes.

Prerequisites

There are many ways to demonstrate how to implement CA. For instance, you could install Kubernetes on your local machine and set up everything manually using the kubectl command-line tool. Or you could set up a user with sufficient permissions on Amazon Web Services (AWS), Google Cloud Platform (GCP) or Azure to play with Kubernetes using your favorite managed cluster provider. Both options are valid; however, they involve a lot of configuration steps that can distract from the main topic: the Kubernetes Cluster Autoscaler.

An easier solution is one that allows the tutorial to focus on understanding the inner workings of CA and not on time-consuming platform configurations, which is what you’ll be learning about here. This solution involves only two requirements: a Linode account and Rancher.

For this tutorial, you’ll need a running Rancher Manager server. Rancher is perfect for demonstrating how CA works, as it allows you to deploy and manage Kubernetes clusters on any provider conveniently from its powerful UI. Moreover, you can deploy it using several providers, including these popular options:

• K3s: If you already have a K3s installation running on your local machine, you can install Rancher with Helm.
• Docker: Another option is to install Rancher on a single node using Docker.
• Rancher Desktop: This is an excellent choice for developers as it installs a single-node K3s cluster with Helm and metrics server, and it sets up the kubectl command-line tool on your local machine. Once ready, you can install Rancher on Rancher Desktop either using Docker or Helm.
• Cloud installation: Another option is to install Rancher on a Kubernetes cluster using your favorite cloud provider.
• Linode account: Linode is a popular cloud provider that makes it easy to demonstrate how CA scales up and down Kubernetes clusters. All you need is to create a personal access token so that Rancher can integrate Linode Kubernetes Engine (LKE) into its Cluster Management Dashboard. If you don’t have a Linode account, you can sign up for free on their website.

If you are curious about a more advanced implementation, we suggest reading the Rancher documentation, which describes how to install Cluster Autoscaler on Rancher using Amazon Elastic Compute Cloud (Amazon EC2) Auto Scaling groups. However, please note that implementing CA is very similar on different platforms, as all solutions leverage Kubernetes Cluster API for their purposes. Something that will be addressed in more detail later.

What is Cluster API, and how does Kubernetes CA leverage it

Cluster API is an open source project for building and managing Kubernetes clusters. It provides a declarative API to define the desired state of Kubernetes clusters. In other words, Cluster API can be used to extend the Kubernetes API to manage clusters across various cloud providers, bare metal installations and virtual machines.

In comparison, Kubernetes CA leverages Cluster API to enable the automatic scaling of Kubernetes clusters in response to changing application demands. CA detects when the capacity of a cluster is insufficient to accommodate the current workload and then requests additional nodes from the cloud provider. CA then provisions the new nodes using Cluster API and adds them to the cluster. In this way, the CA ensures that the cluster has the capacity needed to serve its applications.

Because Rancher supports CA and RKE2, and K3s works with Cluster API, their combination offers the ideal solution for automated Kubernetes lifecycle management from a central dashboard. This is also true for any other cloud provider that offers support for Cluster API.

Link to the Cluster API blog

Implementing CA in Kubernetes

Now that you know what Cluster API and CA are, it’s time to get down to business. Your first task will be to deploy a new Kubernetes cluster using Rancher.

Deploying a new Kubernetes cluster using Rancher

Begin by navigating to your Rancher installation. Once logged in, click on the hamburger menu located at the top left and select Cluster Management:

On the next screen, click on Drivers:

Rancher uses cluster drivers to create Kubernetes clusters in hosted cloud providers.

For Linode LKE, you need to activate the specific driver, which is simple. Just select the driver and press the Activate button. Once the driver is downloaded and installed, the status will change to Active, and you can click on Clusters in the side menu:

With the cluster driver enabled, it’s time to create a new Kubernetes deployment by selecting Clusters | Create:

Then select Linode LKE from the list of hosted Kubernetes providers:

Next, you’ll need to enter some basic information, including a name for the cluster and the personal access token used to authenticate with the Linode API. When you’ve finished, click Proceed to Cluster Configuration to continue:

If the connection to the Linode API is successful, you’ll be directed to the next screen, where you will need to choose a region, Kubernetes version and, optionally, a tag for the new cluster. Once you’re ready, press Proceed to Node pool selection:

This is the final screen before creating the LKE cluster. In it, you decide how many node pools you want to create. While there are no limitations on the number of node pools you can create, the implementation of Cluster Autoscaler for Linode does impose two restrictions, which are listed here:

1. Each LKE Node Pool must host a single node (called Linode).
2. Each Linode must be of the same type (eg 2GB, 4GB and 6GB).

For this tutorial, you will use two node pools, one hosting 2GB RAM nodes and one hosting 4GB RAM nodes. Configuring node pools is easy; select the type from the drop-down list and the desired number of nodes, and then click the Add Node Pool button. Once your configuration looks like the following image, press Create:

You’ll be taken back to the Clusters screen, where you should wait for the new cluster to be provisioned. Behind the scenes, Rancher is leveraging the Cluster API to configure the LKE cluster according to your requirements:

Once the cluster status shows as active, you can review the new cluster details by clicking the Explore button on the right:

At this point, you’ve deployed an LKE cluster using Rancher. In the next section, you’ll learn how to implement CA on it.

Setting up CA

If you’re new to Kubernetes, implementing CA can seem complex. For instance, the Cluster Autoscaler on AWS documentation talks about how to set permissions using Identity and Access Management (IAM) policies, OpenID Connect (OIDC) Federated Authentication and AWS security credentials. Meanwhile, the Cluster Autoscaler on Azure documentation focuses on how to implement CA in Azure Kubernetes Service (AKS), Autoscale VMAS instances and Autoscale VMSS instances, for which you will also need to spend time setting up the correct credentials for your user.

The objective of this tutorial is to leave aside the specifics associated with the authentication and authorization mechanisms of each cloud provider and focus on what really matters: How to implement CA in Kubernetes. To this end, you should focus your attention on these three key points:

1. CA introduces the concept of node groups, also called by some vendors autoscaling groups. You can think of these groups as the node pools managed by CA. This concept is important, as CA gives you the flexibility to set node groups that scale automatically according to your instructions while simultaneously excluding other node groups for manual scaling.
2. CA adds or removes Kubernetes nodes following certain parameters that you configure. These parameters include the previously mentioned node groups, their minimum size, maximum size and more.
3. CA runs as a Kubernetes deployment, in which secrets, services, namespaces, roles and role bindings are defined.

The supported versions of CA and Kubernetes may vary from one vendor to another. The way node groups are identified (using flags, labels, environmental variables, etc.) and the permissions needed for the deployment to run may also vary. However, at the end of the day, all implementations revolve around the principles listed previously: auto-scaling node groups, CA configuration parameters and CA deployment.

With that said, let’s get back to business. After pressing the Explore button, you should be directed to the Cluster Dashboard. For now, you’re only interested in looking at the nodes and the cluster’s capacity.

The next steps consist of defining node groups and carrying out the corresponding CA deployment. Start with the simplest and follow some best practices to create a namespace to deploy the components that make CA. To do this, go to Projects/Namespaces:

On the next screen, you can manage Rancher Projects and namespaces. Under Projects: System, click Create Namespace to create a new namespace part of the System project:

Give the namespace a name and select Create. Once the namespace is created, click on the icon shown here (ie import YAML):

One of the many advantages of Rancher is that it allows you to perform countless tasks from the UI. One such task is to import local YAML files or create them on the fly and deploy them to your Kubernetes cluster.

To take advantage of this useful feature, copy the following code. Remember to replace <PERSONAL_ACCESS_TOKEN> with the Linode token that you created for the tutorial:

---
apiVersion: v1
kind: Secret
metadata:
  name: cluster-autoscaler-cloud-config
  namespace: autoscaler
type: Opaque
stringData:
  cloud-config: |-
    [global]
    linode-token=<PERSONAL_ACCESS_TOKEN>
    lke-cluster-id=88612
    defaut-min-size-per-linode-type=1
    defaut-max-size-per-linode-type=5
    do-not-import-pool-id=88541

    [nodegroup "g6-standard-1"]
    min-size=1
    max-size=4

    [nodegroup "g6-standard-2"]
    min-size=1
    max-size=2

Next, select the namespace you just created, paste the code in Rancher and select Import:

A pop-up window will appear, confirming that the resource has been created. Press Close to continue:

The secret you just created is how Linode implements the node group configuration that CA will use. This configuration defines several parameters, including the following:

• linode-token: This is the same personal access token that you used to register LKE in Rancher.
• lke-cluster-id: This is the unique identifier of the LKE cluster that you created with Rancher. You can get this value from the Linode console or by running the command curl -H "Authorization: Bearer $TOKEN" https://api.linode.com/v4/lke/clusters, where STOKEN is your Linode personal access token. In the output, the first field, id, is the identifier of the cluster.
• defaut-min-size-per-linode-type: This is a global parameter that defines the minimum number of nodes in each node group.
• defaut-max-size-per-linode-type: This is also a global parameter that sets a limit to the number of nodes that Cluster Autoscaler can add to each node group.
• do-not-import-pool-id: On Linode, each node pool has a unique ID. This parameter is used to exclude specific node pools so that CA does not scale them.
• nodegroup (min-size and max-size): This parameter sets the minimum and maximum limits for each node group. The CA for Linode implementation forces each node group to use the same node type. To get a list of available node types, you can run the command curl https://api.linode.com/v4/linode/types.

This tutorial defines two node groups, one using g6-standard-1 linodes (2GB nodes) and one using g6-standard-2 linodes (4GB nodes). For the first group, CA can increase the number of nodes up to a maximum of four, while for the second group, CA can only increase the number of nodes to two.

With the node group configuration ready, you can deploy CA to the respective namespace using Rancher. Paste the following code into Rancher (click on the import YAML icon as before):

---
apiVersion: v1
kind: ServiceAccount
metadata:
  labels:
    k8s-addon: cluster-autoscaler.addons.k8s.io
    k8s-app: cluster-autoscaler
  name: cluster-autoscaler
  namespace: autoscaler
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: cluster-autoscaler
  labels:
    k8s-addon: cluster-autoscaler.addons.k8s.io
    k8s-app: cluster-autoscaler
rules:
  - apiGroups: [""]
    resources: ["events", "endpoints"]
    verbs: ["create", "patch"]
  - apiGroups: [""]
    resources: ["pods/eviction"]
    verbs: ["create"]
  - apiGroups: [""]
    resources: ["pods/status"]
    verbs: ["update"]
  - apiGroups: [""]
    resources: ["endpoints"]
    resourceNames: ["cluster-autoscaler"]
    verbs: ["get", "update"]
  - apiGroups: [""]
    resources: ["nodes"]
    verbs: ["watch", "list", "get", "update"]
  - apiGroups: [""]
    resources:
      - "namespaces"
      - "pods"
      - "services"
      - "replicationcontrollers"
      - "persistentvolumeclaims"
      - "persistentvolumes"
    verbs: ["watch", "list", "get"]
  - apiGroups: ["extensions"]
    resources: ["replicasets", "daemonsets"]
    verbs: ["watch", "list", "get"]
  - apiGroups: ["policy"]
    resources: ["poddisruptionbudgets"]
    verbs: ["watch", "list"]
  - apiGroups: ["apps"]
    resources: ["statefulsets", "replicasets", "daemonsets"]
    verbs: ["watch", "list", "get"]
  - apiGroups: ["storage.k8s.io"]
    resources: ["storageclasses", "csinodes"]
    verbs: ["watch", "list", "get"]
  - apiGroups: ["batch", "extensions"]
    resources: ["jobs"]
    verbs: ["get", "list", "watch", "patch"]
  - apiGroups: ["coordination.k8s.io"]
    resources: ["leases"]
    verbs: ["create"]
  - apiGroups: ["coordination.k8s.io"]
    resourceNames: ["cluster-autoscaler"]
    resources: ["leases"]
    verbs: ["get", "update"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: cluster-autoscaler
  namespace: autoscaler
  labels:
    k8s-addon: cluster-autoscaler.addons.k8s.io
    k8s-app: cluster-autoscaler
rules:
  - apiGroups: [""]
    resources: ["configmaps"]
    verbs: ["create","list","watch"]
  - apiGroups: [""]
    resources: ["configmaps"]
    resourceNames: ["cluster-autoscaler-status", "cluster-autoscaler-priority-expander"]
    verbs: ["delete", "get", "update", "watch"]

---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: cluster-autoscaler
  labels:
    k8s-addon: cluster-autoscaler.addons.k8s.io
    k8s-app: cluster-autoscaler
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: cluster-autoscaler
subjects:
  - kind: ServiceAccount
    name: cluster-autoscaler
    namespace: autoscaler

---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: cluster-autoscaler
  namespace: autoscaler
  labels:
    k8s-addon: cluster-autoscaler.addons.k8s.io
    k8s-app: cluster-autoscaler
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: cluster-autoscaler
subjects:
  - kind: ServiceAccount
    name: cluster-autoscaler
    namespace: autoscaler

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: cluster-autoscaler
  namespace: autoscaler
  labels:
    app: cluster-autoscaler
spec:
  replicas: 1
  selector:
    matchLabels:
      app: cluster-autoscaler
  template:
    metadata:
      labels:
        app: cluster-autoscaler
      annotations:
        prometheus.io/scrape: 'true'
        prometheus.io/port: '8085'
    spec:
      serviceAccountName: cluster-autoscaler
      containers:
        - image: k8s.gcr.io/autoscaling/cluster-autoscaler-amd64:v1.26.1
          name: cluster-autoscaler
          resources:
            limits:
              cpu: 100m
              memory: 300Mi
            requests:
              cpu: 100m
              memory: 300Mi
          command:
            - ./cluster-autoscaler
            - --v=2
            - --cloud-provider=linode
            - --cloud-config=/config/cloud-config
          volumeMounts:
            - name: ssl-certs
              mountPath: /etc/ssl/certs/ca-certificates.crt
              readOnly: true
            - name: cloud-config
              mountPath: /config
              readOnly: true
          imagePullPolicy: "Always"
      volumes:
        - name: ssl-certs
          hostPath:
            path: "/etc/ssl/certs/ca-certificates.crt"
        - name: cloud-config
          secret:
            secretName: cluster-autoscaler-cloud-config

In this code, you’re defining some labels; the namespace where you will deploy the CA; and the respective ClusterRole, Role, ClusterRoleBinding, RoleBinding, ServiceAccount and Cluster Autoscaler.

The difference between cloud providers is near the end of the file, at command. Several flags are specified here. The most relevant include the following:

• Cluster Autoscaler version v.
• cloud-provider; in this case, Linode.
• cloud-config, which points to a file that uses the secret you just created in the previous step.

Again, a cloud provider that uses a minimum number of flags is intentionally chosen. For a complete list of available flags and options, read the Cloud Autoscaler FAQ.

Once you apply the deployment, a pop-up window will appear, listing the resources created:

You’ve just implemented CA on Kubernetes, and now, it’s time to test it.

CA in action

To check to see if CA works as expected, deploy the following dummy workload in the default namespace using Rancher:

Here’s a review of the code:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: busybox-workload
  labels:
    app: busybox
spec:
  replicas: 600
  strategy:
    type: RollingUpdate
  selector:
    matchLabels:
      app: busybox
  template:
    metadata:
      labels:
        app: busybox
    spec:
      containers:
      - name: busybox
        image: busybox
        imagePullPolicy: IfNotPresent
        
        command: ['sh', '-c', 'echo Demo Workload ; sleep 600']

As you can see, it’s a simple workload that generates 600 busybox replicas.

If you navigate to the Cluster Dashboard, you’ll notice that the initial capacity of the LKE cluster is 220 pods. This means CA should kick in and add nodes to cope with this demand:

If you now click on Nodes (side menu), you will see how the node-creation process unfolds:

If you wait a couple of minutes and go back to the Cluster Dashboard, you’ll notice that CA did its job because, now, the cluster is serving all 600 replicas:

This proves that scaling up works. But you also need to test to see scaling down. Go to Workload (side menu) and click on the hamburger menu corresponding to busybox-workload. From the drop-down list, select Delete:

A pop-up window will appear; confirm that you want to delete the deployment to continue:

By deleting the deployment, the expected result is that CA starts removing nodes. Check this by going back to Nodes:

Keep in mind that by default, CA will start removing nodes after 10 minutes. Meanwhile, you will see taints on the Nodes screen indicating the nodes that are candidates for deletion. For more information about this behavior and how to modify it, read “Does CA respect GracefulTermination in scale-down?” in the Cluster Autoscaler FAQ.

After 10 minutes have elapsed, the LKE cluster will return to its original state with one 2GB node and one 4GB node:

Optionally, you can confirm the status of the cluster by returning to the Cluster Dashboard:

And now you have verified that Cluster Autoscaler can scale up and down nodes as required.

CA, Rancher and managed Kubernetes services

At this point, the power of Cluster Autoscaler is clear. It lets you automatically adjust the number of nodes in your cluster based on demand, minimizing the need for manual intervention.

Since Rancher fully supports the Kubernetes Cluster Autoscaler API, you can leverage this feature on major service providers like AKS, Google Kubernetes Engine (GKE) and Amazon Elastic Kubernetes Service (EKS). Let’s look at one more example to illustrate this point.

Create a new workload like the one shown here:

It’s the same code used previously, only in this case, with 1,000 busybox replicas instead of 600. After a few minutes, the cluster capacity will be exceeded. This is because the configuration you set specifies a maximum of four 2GB nodes (first node group) and two 4GB nodes (second node group); that is, six nodes in total:

Head over to the Linode Dashboard and manually add a new node pool:

The new node will be displayed along with the rest on Rancher’s Nodes screen:

Better yet, since the new node has the same capacity as the first node group (2GB), it will be deleted by CA once the workload is reduced.

In other words, regardless of the underlying infrastructure, Rancher makes use of CA to know if nodes are created or destroyed dynamically due to load.

Overall, Rancher’s ability to support Cluster Autoscaler out of the box is good news; it reaffirms Rancher as the ideal Kubernetes multi-cluster management tool regardless of which cloud provider your organization uses. Add to that Rancher’s seamless integration with other tools and technologies like Longhorn and Harvester, and the result will be a convenient centralized dashboard to manage your entire hyper-converged infrastructure.

Conclusion

This tutorial introduced you to Kubernetes Cluster Autoscaler and how it differs from other types of autoscaling, such as Vertical Pod Autoscaler (VPA) and Horizontal Pod Autoscaler (HPA). In addition, you learned how to implement CA on Kubernetes and how it can scale up and down your cluster size.

Finally, you also got a brief glimpse of Rancher’s potential to manage Kubernetes clusters from the convenience of its intuitive UI. Rancher is part of the rich ecosystem of SUSE, the leading open Kubernetes management platform. To learn more about other solutions developed by SUSE, such as Edge 2.0 or NeuVector, visit their website.

Cloud deployments and containerization let you provision infrastructure as needed, meaning your applications can grow in scope and complexity. The results can be impressive, but the ability to expand quickly and easily makes it harder to keep track of your system as it develops.

In this type of Kubernetes deployment, it’s essential to track your containers to understand what they’re doing. You need to not only monitor your system but also ensure your monitoring delivers meaningful observability. The numbers you track need to give you actionable insights into your applications.

In this article, you’ll learn why monitoring and observability matter and how you can best take advantage of them. That way, you can get all the information you need to maximize the performance of your deployments.

Why you need monitoring and observability in Kubernetes

Monitoring and observability are often confused but worth clarifying for the purposes of this discussion. Monitoring is the means by which you gain information about what your system is doing.

Observability is a more holistic term, indicating the overall capacity to view and understand what is happening within your systems. Logs, metrics and traces are core elements. Essentially, observability is the goal, and monitoring is the means.

Observability can include monitoring as well as logging, tracing, continuous integration and even chaos engineering. Focusing on each facet gets you as close as possible to full coverage. Correcting that can improve your observability if you’ve overlooked one of these areas.

In addition, using black boxes, such as third-party services, can limit observability by making monitoring harder. Increasing complexity can also add problems. Your metrics may not be consistent or relevant if collected from different services or regions.

You need to work to ensure the metrics you collect are taken in context and can be used to provide meaningful insights into where your systems are succeeding and failing.

At a higher level, there are several uses for monitoring and observability. Performance monitoring tells you whether your apps are delivering quickly and what resources they’re consuming.

Issue tracking is also important. Observability can be focused on specific tasks, letting you see how well they’re doing. This can be especially relevant when delivering a new feature or hunting a bug.

Improving your existing applications is also vital. Examining your metrics and looking for areas you can improve will help you stay competitive and minimize your costs. It can also prevent downtime if you identify and fix issues before they lead to performance drops or outages.

Best practices and tips for monitoring and observability in Kubernetes

With distributed applications, collecting data from all your various nodes and containers is more involved than with a standard server-based application. Your tools need to handle the additional complexity.

The following tips will help you build a system that turns information into the elusive observability that you need. All that data needs to be tracked, stored and consolidated. After that, you can use it to gain the insights you need to make better decisions for the future of your application.

Avoid vendor lock-in

The major Kubernetes management services, including Amazon Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS) and Google Kubernetes Engine (GKE), provide their own monitoring tools. While these tools include useful features, you need to beware of becoming overdependent on any that belong to a particular platform, which can lead to vendor lock-in. Ideally, you should be able to change technologies and keep the majority of your metric-gathering system.

Rancher, a complete software stack, lets you consolidate information from other platforms that can help solve issues arising when companies use different technologies without integrating them seamlessly. It lets you capture data from a wealth of tools and pipe your logs and data to external management platforms, such as Grafana and Prometheus, meaning your monitoring isn’t tightly coupled to any other part of your infrastructure. This gives you the flexibility to swap parts of your system in and out without too much expense. With platform-agnostic monitoring tools, you can replace other parts of your system more easily.

Pick the right metrics

Collecting metrics sounds straightforward, but it requires careful implementation. Which metrics do you choose? In a Kubernetes deployment, you need to ensure all layers of your system are monitored. That includes the application, the control plane components and everything in between.

CPU and memory usage are important but can be tricky to use across complex deployments. Other metrics, such as API response, request and error rates, along with latency, can be easier to track and give a more accurate picture of how your apps are performing. High disk utilization is a key indicator of problems with your system and should always be monitored.

At the cluster level, you should track node availability and how many running pods you have and make sure you aren’t in danger of running out of nodes. Nodes can sometimes fail, leaving you short.

Within individual pods, as well as resource utilization, you should check application-specific metrics, such as active users or parts of your app that are in use. You also need to track the metrics Kubernetes provides to verify pod health and availability.

Centralize your logging

Kubernetes pods keep their own logs, but having logs in different places is hard to keep track of. In addition, if a pod crashes, you can lose them. To prevent the loss, make sure any logs or metrics you require for observability are stored in an independent, central repository.

Rancher can help with this by giving you a central management point for your containers. With logs in one place, you can view the data you need together. You can also make sure it is backed up if necessary.

In addition to piping logs from different clusters to the same place, Rancher can also help you centralize authorization and give you coordinated role-based access control (RBAC).

Transferring large volumes of data will have a performance impact, so you need to balance your requirements with cost. Critical information should be logged immediately, but other data can be transferred on a regular basis, perhaps using a queued operation or as a scheduled management task.

Enforce data correlation

Once you have feature-rich tools in place and, therefore, an impressive range of metrics to monitor and elaborate methods for viewing them, it’s easy to lose focus on the reason you’re collecting the data.

Ultimately, your goal is to improve the user experience. To do that, you need to make sure the metrics you collect give you an accurate, detailed picture of what the user is experiencing and correctly identify any problems they may be having.

Lean toward this in the metrics you pick and in those you prioritize. For example, you might want to track how many people who use your app are actually completing actions on it, such as sales or logins.

You can track these by monitoring task success rates as well as how long actions take to complete. If you see a drop in activity on a particular node, that can indicate a technical problem that your other metrics may not pick up.

You also need to think about your alerting systems and pick alerts that spot performance drops, preferably detecting issues before your customers.

With Kubernetes operating in a highly dynamic way, metrics in different pods may not directly correspond to one another. You need to contextualize different results and develop an understanding of how performance metrics correspond to the user’s experience and business outcomes.

Artificial intelligence (AI) driven observability tools can help with that, tracking millions of data points and determining whether changes are caused by the dynamic fluctuations that happen in massive, scaling deployments or whether they represent issues that need to be addressed.

If you understand the implications of your metrics and what they mean for users, then you’re best suited to optimize your approach.

Favor scalable observability solutions

As your user base grows, you need to deal with scaling issues. Traffic spikes, resource usage and latency all need to be kept under control. Kubernetes can handle some of that for you, but you need to make sure your monitoring systems are scalable as well.

Implementing observability is especially complex in Kubernetes because Kubernetes itself is complicated, especially in multi-cloud deployments. The complexity has been likened to an iceberg.

It gets more difficult when you have to consider problems that arise when you have multiple servers duplicating functionality around the world. You need to ensure high availability and make your database available everywhere. As your deployment scales up, so do these problems.

Rancher’s observability tools allow you to deploy new clusters and monitor them along with your existing clusters from the same location. You don’t need to work to keep up as you deploy more widely. That allows you to focus on what your metrics are telling you and lets you spend your time adding more value to your product.

Conclusion

Kubernetes enables complex deployments, but that means monitoring and observability aren’t as straightforward as they would otherwise be. You need to take special care to ensure your solutions give you an accurate picture of what your software is doing.

Taking care to pick the right metrics makes your monitoring more helpful. Avoiding vendor lock-in gives you the agility to change your setup as needed. Centralizing your metrics brings efficiency and helps you make critical big-picture decisions.

Enforcing data correlation helps keep your results relevant, and thinking about scalability ahead of time stops your system from breaking down when things change.

Rancher can help and makes managing Kubernetes clusters easier. It provides a vast range of Kubernetes monitoring and observability features, ensuring you know what’s going on throughout your deployments. Check it out and learn how it can help you grow. You can also take advantage of free, community training for Kubernetes & Rancher at the Rancher Academy.

Fleet, also known as “Continuous Delivery” in Rancher, deploys application workloads across multiple clusters. However, most applications need configuration and credentials. In Kubernetes, we store confidential information in secrets. For Fleet’s deployments to work on downstream clusters, we need to create these secrets on the downstream clusters themselves.

When planning multi-cluster deployments, our users ask themselves: “I won’t embed confidential information in the Git repository for security reasons. However, managing the Kubernetes secrets manually does not scale as it is error prone and complicated. Can Fleet help me solve this problem?”

To ensure Fleet deployments work seamlessly on downstream clusters, we need a streamlined approach to create and manage these secrets across clusters.
A wide variety of tools exists for Kubernetes to manage secrets, e.g., the SOPS operator and the external secrets operator.

A previous blog post showed how to use the external-secrets operator (ESO) together with the AWS secret manager to create sealed secrets.

ESO supports a wide range of secret stores, from Vault to Google Cloud Secret Manager and Azure Key Vault. This article uses the Kubernetes secret store on the control plane cluster to create derivative secrets on a number of downstream clusters, which can be used when we deploy applications via Fleet. That way, we can manage secrets without any external dependency.

We will have to deploy the external secrets operator on each downstream cluster. We will use Fleet to deploy the operator, but each operator needs a secret store configuration. The configuration for that store could be deployed via Fleet, but as it contains credentials to the upstream cluster, we will create it manually on each cluster.

As a prerequisite, we need to gather the control plane’s API server URL and certificate.

Let us assume the API server is reachable on “YOUR-IP.sslip.io”, e.g., “192.168.1.10.sslip.io:6443”. You might need a firewall exclusion to reach that port from your host.

export API_SERVER=https://192.168.1.10.sslip.io:6443

Deploying the External Secrets Operator To All Clusters

Note: Instead of pulling secrets from the upstream cluster, an alternative setup would install ESO only once and use PushSecrets to write secrets to downstream clusters. That way we would only install one External Secrets Operator and give the upstream cluster access to each downstream cluster’s API server.

Since we don’t need a git repository for ESO, we’re installing it directly to the downstream Fleet clusters in the fleet-default namespace by creating a bundle.

Instead of creating the bundle manually, we convert the Helm chart with the Fleet CLI. Run these commands:

cat > targets.yaml <<EOF
targets:
- clusterSelector: {}
EOF

mkdir app
cat > app/fleet.yaml <<EOF
defaultNamespace: external-secrets
helm:
  repo: https://charts.external-secrets.io
  chart: external-secrets
EOF

fleet apply --compress --targets-file=targets.yaml -n fleet-default -o - external-secrets app > eso-bundle.yaml

Then we apply the bundle:

kubectl apply -f eso-bundle.yaml

Each downstream cluster now has one ESO installed.

Make sure you use a cluster selector in targets.yaml, that matches all clusters you want to deploy to.

Create a Namespace for the Secret Store

We will create a namespace that holds the secrets on the upstream cluster. We also need a service account with a role binding to access the secrets. We use the role from the ESO documentation.

kubectl create ns eso-data
kubectl apply -n eso-data -f - <<EOF
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: eso-store-role
rules:
- apiGroups: [""]
  resources:
  - secrets
  verbs:
  - get
  - list
  - watch
- apiGroups:
  - authorization.k8s.io
  resources:
  - selfsubjectrulesreviews
  verbs:
  - create
EOF
kubectl create -n eso-data serviceaccount upstream-store
kubectl create -n eso-data rolebinding upstream-store --role=eso-store-role --serviceaccount=eso-data:upstream-store
token=$( kubectl create -n eso-data token upstream-store )

Add Credentials to the Downstream Clusters

We could use a Fleet bundle to distribute the secret to each downstream cluster, but we don’t want credentials outside of k8s secrets. So, we use kubectl on each cluster manually. The token was added to the shell’s environment variable so we don’t leak it in the host’s process list when we run:

for ctx in downstream1 downstream2 downstream3; do 
  kubectl --context "$ctx" create secret generic upstream-token --from-literal=token="$token"
done

Assuming we have the given kubectl contexts in our kubeconfig. You can check with kubectl config get-contexts.

Configure the External Secret Operators

We need to configure the ESOs to use the upstream cluster as a secret store. We will also provide the CA certificate to access the API server. We create another Fleet bundle and re-use the target.yaml from before.

mkdir cfg
ca=$( kubectl get cm -n eso-data kube-root-ca.crt -o go-template='{{index .data "ca.crt"}}' )
kubectl create cm --dry-run=client upstream-ca --from-literal=ca.crt="$ca" -oyaml > cfg/ca.yaml

cat > cfg/store.yaml <<EOF
apiVersion: external-secrets.io/v1beta1
kind: SecretStore
metadata:
  name: upstream-store
spec:
  provider:
    kubernetes:
      remoteNamespace: eso-data
      server:
        url: "$API_SERVER"
        caProvider:
          type: ConfigMap
          name: upstream-ca
          key: ca.crt
      auth:
        token:
          bearerToken:
            name: upstream-token
            key: token
EOF

fleet apply --compress --targets-file=targets.yaml -n fleet-default -o - external-secrets cfg > eso-cfg-bundle.yaml

Then we apply the bundle:

kubectl apply -f eso-cfg-bundle.yaml

Request a Secret from the Upstream Store

We create an example secret in the upstream cluster’s secret store namespace.

kubectl create secret -n eso-data generic database-credentials --from-literal username="admin" --from-literal password="$RANDOM"

On any of the downstream clusters, we create an ExternalSecret resource to copy from the store. This will instruct the External-Secret Operator to copy the referenced secret from the upstream cluster to the downstream cluster.

Note: We could have included the ExternalSecret resource in the cfg bundle.

kubectl apply -f - <<EOF
apiVersion: external-secrets.io/v1beta1
kind: ExternalSecret
metadata:
  name: database-credentials
spec:
  refreshInterval: 1m
  secretStoreRef:
    kind: SecretStore
    name: upstream-store
  target:
    name: database-credentials
  data:
  - secretKey: username
    remoteRef:
      key: database-credentials
      property: username
  - secretKey: password
    remoteRef:
      key: database-credentials
      property: password
EOF

This should create a new secret in the default namespace. You can check the k8s event log for problems with kubectl get events.

 

We can now use the generated secrets to pass credentials as helm values into Fleet multi-cluster deployments, e.g., to use a database or an external service with our workloads.

Introduction

As organizations increasingly adopt containerized applications, it is essential to understand what container orchestration is. This guide delves into what container orchestration is, its benefits, and how it works, comparing popular platforms like Kubernetes and Docker. We will also discuss multi-cloud container orchestration and the role of Rancher Prime in simplifying container orchestration management.

What is Container Orchestration?

Container orchestration is the process of managing the lifecycle of containers within a distributed environment. Containers are lightweight, portable, and scalable units for packaging and deploying applications, providing a consistent environment, and reducing the complexity of managing dependencies. Container orchestration automates the deployment, scaling, and management of these containers, ensuring the efficient use of resources, improving reliability, and facilitating seamless updates.

How Does Container Orchestration Work?

Container orchestration works by coordinating container deployment across multiple host machines or clusters. Orchestration platforms utilize a set of rules and policies to manage container lifecycles, which include:

• Scheduling: Allocating containers to available host resources based on predefined constraints and priorities.
• Service discovery: Identifying and connecting containers to form a cohesive application.
• Load balancing: Distributing network traffic evenly among containers to optimize resource usage and improve application performance.
• Scaling: Dynamically adjusting the number of container instances based on application demand.
• Health monitoring: Monitoring container performance and replacing failed containers with new ones.
• Configuration management: Ensuring consistent configuration across all containers in the application.
• Networking: Managing the communication between containers and external networks.

Why Do You Need Container Orchestration?

Container orchestration is essential for organizations that deploy and manage applications in a containerized environment. It addresses the challenges that arise when managing multiple containers, such as:

• Scaling applications efficiently to handle increased workloads.
• Ensuring high availability and fault tolerance by detecting and replacing failed containers.
• Facilitating seamless updates and rollbacks.
• Managing and maintaining container configurations.
• Optimizing resource usage and application performance.

What are The Benefits of Container Orchestration?

Container orchestration offers several advantages, including:

• Improved efficiency: Container orchestration optimizes resource usage, reducing infrastructure costs.
• Enhanced reliability: By monitoring container health and automatically replacing failed containers, orchestration ensures application availability and fault tolerance.
• Simplified management: Orchestration automates container deployment, scaling, and management, reducing the manual effort required.
• Consistency: Orchestration platforms maintain consistent configurations across all containers, eliminating the risk of configuration drift.
• Faster deployment: Orchestration streamlines application deployment, enabling organizations to bring new features and updates to market more quickly.

What is Kubernetes Container Orchestration?

Kubernetes is an open-source container orchestration platform developed by Google. It automates the deployment, scaling, and management of containerized applications. Kubernetes organizes containers into groups called “pods” and manages them using a declarative approach, where users define the desired state of the application, and Kubernetes works to maintain that state. Key components of Kubernetes include:

• API server: The central management point for Kubernetes, providing a RESTful API for communication with the system.
• etcd: A distributed key-value store that stores the configuration data for the Kubernetes cluster.
• kubelet: An agent that runs on each worker node, ensuring containers are running as defined in the desired state.
• kubectl: A command-line tool for interacting with the Kubernetes API server.

What is Multi-Cloud Container Orchestration?

Multi-cloud container orchestration is the management of containerized applications across multiple cloud providers. Organizations often use multiple clouds to avoid vendor lock-in, increase application resilience and leverage specific cloud services or features. Multi-cloud orchestration enables organizations to:

• Deploy applications consistently across different cloud providers.
• Optimize resource usage and cost by allocating containers to the most suitable cloud environment.
• Enhance application resilience and availability by distributing workloads across multiple clouds.
• Simplify management and governance of containerized applications in a multi-cloud environment.

Docker Container Orchestration vs. Kubernetes Container Orchestration

Docker and Kubernetes are both popular container orchestration platforms, each with its strengths and weaknesses.

Docker:

• Developed by Docker Inc., the same company behind the Docker container runtime.
• Docker Swarm is the native container orchestration tool for Docker.
• Easier to set up and manage, making it suitable for small-scale deployments.
• Limited scalability compared to Kubernetes.
• Lacks advanced features like auto-scaling and rolling updates.

Kubernetes:

• Developed by Google and now owned by the CNCF, with a large community and ecosystem.
• More feature-rich, including auto-scaling, rolling updates, and self-healing.
• Higher complexity, with a steeper learning curve than Docker Swarm.
• Highly scalable, making it suitable for large-scale deployments and enterprises.
• Widely adopted and supported by major cloud providers.

Container Orchestration Platforms

Several container orchestration platforms are available, including:

• Kubernetes: An open-source, feature-rich, and widely adopted enterprise container orchestration platform.
• Docker Swarm: Docker’s native container orchestration tool, suitable for small-scale deployments.
• Amazon ECS (Elastic Container Service): A managed container orchestration service provided by AWS.
• HashiCorp Nomad: A simple scheduler and orchestrator to deploy and manage containers and non-containerized applications across on-prem and clouds at scale.

Considerations When Implementing Container Orchestration

Before implementing container orchestration, organizations should consider the following factors:

• Scalability: Choose an orchestration platform that can handle the anticipated workload and scale as needed.
• Complexity: Assess the learning curve and complexity of the orchestration platform, ensuring it aligns with the team’s expertise.
• Integration: Ensure the orchestration platform integrates well with existing tools, services, and infrastructure.
• Vendor lock-in: Evaluate the potential for vendor lock-in and consider toolsets that support multi-cloud strategies to mitigate this risk.
• Support and community: Assess both enterprise support and community resources available for the chosen orchestration platform.
• Sustainability: Ensure the long-term sustainability of your chosen platform.
• Security: integrate proven security frameworks like Secure Software Supply Chain and Zero Trust early in the platform design process.

How Rancher Prime Can Help

Rancher Prime is an open-source container management platform that simplifies Kubernetes management and deployment. Rancher Prime provides a user-friendly interface for securely managing container orchestration across multiple clusters and cloud providers. Key features of Rancher Prime include:

• Centralized management: Manage multiple Kubernetes clusters from a single dashboard.
• Multi-cloud support: Deploy and manage Kubernetes clusters on various cloud providers and on-premises environments.
• Integrated tooling: Rancher Prime integrates with popular tools for logging, monitoring, and continuous integration/continuous delivery (CI/CD).
• Security: Rancher Prime provides built-in security features, including FIPS encryption, STIG certification, a centralized authentication proxy, role-based access control, pod security admission, network policies, enhanced policy management engine, …
• Enhanced security: when Rancher Prime is combined with container native security by SUSE NeuVector, you can fully implement Zero Trust for enterprise grade container orchestration hardening.
• Simplified cluster operations: Rancher Prime simplifies cluster deployment, scaling, and upgrades, either through and easy to learn API or by leveraging industry standards like Cluster API.
• Support and community: Since 1992 SUSE provide specialized enterprise support and it works closely with organizations like CNCF to provide community validated solutions. SUSE owns the Rancher Prime product suite and is an active contributor to the CNCF having donated projects like: K3s, Longhorn and Kubewarden.

Conclusion

Container orchestration is a critical component for managing containerized applications in a distributed environment. Understanding the differences between platforms like Kubernetes and Docker, as well as the benefits of multi-cloud orchestration, can help organizations make informed decisions about their container orchestration strategy. Rancher Prime offers a powerful solution for simplifying the management and deployment of container orchestration in any scenario, making it easier for organizations to reap the benefits of containerization.

We are pleased to announce that a new Rancher Desktop version with additional configuration options, deployment profiles, a gvisor-based networking stack on Windows and several other improvements has just been released!

Application behavior configuration

First up, we added preference options that make it easy to configure the application behavior. For example, you can configure Rancher Desktop to start when you log in to your machine automatically, customize whether to show/hide the application GUI on startup, etc. We believe these features will enhance your overall experience of using Rancher Desktop. Try them out via the GUI or rdctl CLI and let us know what you think. For example, you can use the rdctl command below to start Rancher Desktop in the background and with dockerd(moby) as the selected container engine. 

rdctl start --application.start-in-background=true --container-engine.name moby

Deployment profiles (experimental)

Are you an IT Administrator looking for ways to have a consistent Rancher Desktop setup across multiple user machines by enforcing predefined settings and configurations? Are you that user who frequently wants to start over with a factory reset in the rapid experimentation process but wishes there was a way to avoid the initial configuration? We understand you. The experimental deployment profiles feature introduced in the release is just for you.

The deployment profiles feature lets you leverage standard OS mechanisms such as Windows registry and macOS plists to store and load Rancher Desktop preference settings efficiently. For example, you can use a default deployment profile to set your preferred container runtime as default so that you don’t have to manually configure this setting during the first launch or after a factory reset. Similarly, you can use a locked deployment profile to enforce policies around container image access in your organization. 

The picture above shows the allowed images list coming from a locked deployment profile which will enable users to access only a specific set of images, which in this case are:

• Images in an organization namespace myorg on DockerHub 
• Docker official images ( docker.io/library/)
• Images from publishers you trust on DockerHub (ex: rancher/, google/, grafana/)
• Images from registries you trust (ex: registry.suse.com, registry.rancher.com)

New gvisor-based networking stack (experimental)

If you are a Windows user who has been unable to use certain networking-dependent features of Rancher Desktop due to an incompatible VPN setup at your organization, then we have some good news for you. We have introduced an experimental gvisor-based networking stack on Windows that should provide better compatibility with diverse VPN configurations. The initial phase of the implementation for Windows has been rolled out in this release. We are committed to extending it to other platforms and making it more robust in future releases. You can enable the new stack using the command rdctl set --experimental.virtual-machine.networking-tunnel=true and learn more about the implementation, limitations, etc., here. Also, please provide feedback if you encounter problems beyond the documented limitations. 

Other key features in Rancher Desktop 1.8

• Support for Apple Virtualization framework on macOS (experimental). Try it with the command rdctl set --virtual-machine.type vz 
• The alternate filesystem protocol 9p can now be selected on macOS via the rdctl command rdctl set--experimental.virtual-machine.mount.type 9p
• Additional mount points (/Volumes and /var/folders) are available on macOS 
• socket_vmnet has been updated on macOS (experimental). Select it with the command rdctl set --experimental.virtual-machine.socket-fmnet=true

Next steps

There are several next steps you can take: 

• Learn more about the changes in the 1.8.0 release from the release notes. 
• Star Rancher Desktop on GitHub to show that you like it. This lets us know that you want to see development continue. 
• Install the v1.8 release. 
• Provide feedback in the issue queue. 

Rancher is a Kubernetes management platform that creates a consistent environment for multicloud container operation. It solves several of the challenges around multicloud Kubernetes deployments, such as poor visibility into where workloads are running and the lack of centralized authentication and access control.

Multicloud improves resiliency by letting you distribute applications across providers. It can also be a competitive advantage since you’re able to utilize the benefits of every provider. Moreover, multicloud reduces vendor lock-in because you’re less dependent on any one platform.

However, these advantages are often negated by the difficulty in managing multi-cloud Kubernetes. Deploying multiple clusters, using them as one unit and monitoring the entire fleet are daunting tasks for team leaders. You need a way to consistently implement authorization, observability and security best practices.

In this article, you’ll learn how Rancher resolves these problems so you can confidently use Kubernetes in multi-cloud scenarios.

Rancher and multicloud

One of the benefits of Rancher is that it provides a consistent experience when you’re using several environments. You can manage the full lifecycle of all your clusters, whether they’re in the cloud or on-premises. It also abstracts away the differences between Kubernetes implementations, creating a single surface for monitoring your deployments.

Rancher is flexible enough to work with both new and existing clusters, and there are three possible ways to connect your clusters:

1. Provision a new cluster using a managed cloud Kubernetes service:Rancher can create new Amazon Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS) and Google Kubernetes Engine (GKE) clusters for you. The process is fully automated within the Rancher UI. You can also import existing clusters.
2. Provision a new cluster on standalone cloud infrastructure: Rancher can deploy an RKE, RKE2, or K3s cluster by provisioning new compute nodes from your cloud provider. This option supports Amazon Elastic Compute Cloud (EC2), Microsoft Azure, DigitalOcean, Harvester, Linode and VMware vSphere.
3. Bring your own cluster: You can manually connect Kubernetes clusters running locally or in other cloud environments. This gives you the versatility to combine on-premises and public cloud infrastructure in hybrid deployment situations.

Once you’ve added your multicloud clusters, your single Rancher installation lets you seamlessly manage them all.

A unified dashboard

One of the biggest multicloud headaches is tracking what’s deployed, where it’s located and whether it’s running correctly. With Rancher, you get a unified dashboard that shows every cluster, including the cloud environment it’s hosted in and its resource utilization:

The Rancher home screen provides a centralized view of the clusters you’ve registered, covering both your cloud and on-premises deployments. Similarly, the sidebar integrates a shortcut list of clusters that helps you quickly move between environments.

After you’ve navigated to a specific cluster, the Cluster Dashboard page offers an at-a-glance view of capacity, utilization, events and deployments:

Scrolling further down, you can view precise cluster metrics that help you analyze performance:

Rancher lets you access vital monitoring data for all your Kubernetes environments within one tool, eliminating the need to log into individual cloud provider control panels.

Centralized authorization and access control

Kubernetes has built-in support for role-based access control (RBAC) to limit the actions that individual user accounts can take. However, this is insufficient for multicloud deployments because you have to manage and maintain your policies individually in each of your clusters.

Rancher improves multicloud Kubernetes usability by adding a centralized user authentication system. You can set up user accounts within Rancher or connect an external service using protocols such as LDAP, SAML and OAuth.

Once you’ve created your users, you can assign them specific access control rules to limit their rights within Rancher and your clusters. Global permissionsdefine how users can manage your Rancher installation. For instance, you can create and modify cluster connections while cluster- and project-level rolesconfigure the available actions after selecting a cluster.

To create a new user, click the menu icon in the top-left to expand the sidebar, then select the Users & Authentication link. Press the Create button on the next screen, where your existing users are displayed:

Fill out your new user’s credentials on the following screen:

Then scroll down the page to begin assigning permissions to the new user.

Set the user’s global permissions, which control their overall level of access within Rancher. Then you can add more fine-grained policies for specific actions from the roles at the bottom. Once you’ve finished, click the Create button on the bottom-right to add the account. The user can now log into Rancher:

Next, navigate to one of your clusters and head to Cluster > Cluster Membersin the sidebar. Click the Add button in the top-right to grant a user access to the cluster:

Use the next screen to search for the user account, then set their role in the cluster. Once you press Create in the bottom-right, the user will be able to perform the cluster interactions you’ve assigned:

Adding a cluster role

For more precise access control, you can set up your own roles that build upon Kubernetes RBAC. These can apply at the global (Rancher) level or within a specific cluster or project/namespace. All three are created in a similar way.

To create a cluster role, expand the Rancher sidebar again and return to the Users & Authentication page. Select the Roles link from the menu on the left and then select Cluster from the tab strip. Press the Create Cluster Rolebutton in the top-right:

Give your role a name and enter an optional description. Next, use the Grant Resources interface to define the Kubernetes permissions the role includes. This example permits users to create and list pods in the cluster. Press the Create button to add your role:

The role will now show up when you’re adding new members to your clusters:

Rancher and multicloud security

Rancher enhances multicloud security by providing active mechanisms for tightening your environments. Besides the security benefits of centralized authentication and RBAC, Rancher also integrates additional security measuresthat protect your clusters and cloud environments.

Rancher maintains a comprehensive hardening guide based on the Center for Internet Security (CIS) Benchmarks that help you implement best practices and identify vulnerabilities. You can scan a cluster against the benchmark from within the Rancher application.

To do so, navigate to your cluster, then expand Apps > Charts in the left sidebar. Select the CIS Benchmark chart from the list:

Click the Install button on the next screen:

Follow the steps to complete the installation in your cluster:

It could take several minutes for the process to finish — you’ll see a “SUCCESS” message in the logs pane when it’s done:

Now, navigate back to your cluster. You’ll find a new CIS Benchmark item in Rancher’s sidebar. Expand this menu and click the Scan link; then press the Create button on the page that appears:

On the next screen, you’ll be prompted to select a scan profile. This defines the hardening checks that will be performed. You can change the default to choose a different benchmark or Kubernetes version. Press the Create button to start the scan:

The scan run will then show in the Scans table back on the CIS Benchmark > Scan screen:

Once it is finished, you can view the results in your browser by selecting the scan from the table:

Rancher helps DevOps teams to scale multicloud environments

Multicloud is hard — more resources normally means higher overheads, a bigger attack surface and a rapidly swelling toolchain. These issues can impede you as you try to scale.

Rancher incorporates unique capabilities that help operators work effectively with different deployments, even when they’re distributed across several environments.

Automatic cluster backups provide safety

Rancher includes a backup system that you can install as an operator in your clusters. This operator backs up your Kubernetes API resources so you can recover from disasters.

You can add the operator by navigating to a cluster and choosing Apps > Charts from the side menu. Then find the Rancher Backups app and follow the prompts to install it:

You’ll find the Rancher Backups item appear in the navigation menu. Click the Create button to define a new one-time or recurring backup schedule:

Fill out the details to configure your backup routine:

Once you’ve created a backup, you can restore it in the future if data gets accidentally deleted or a disaster occurs. With Rancher, you can create backups for all your clusters with a single consistent procedure, which produces more resilient environments.

Rancher integrates with multi-cloud solutions

One of the benefits of Rancher is that it’s built as a single platform for managing Kubernetes in any cluster. But it gets even better when combined with other ecosystem tools. Rancher has integrations with adjacent components that provide more focused support for specific use cases, including the following:

• Longhorn is distributed Cloud native block storage that runs anywhere and supports automated provisioning, security and backups. You can deploy Longhorn to your clusters from within the Rancher UI, enabling more reliable storage for your workloads.
• Harvester is a solution for hyperconverged infrastructure on bare-metal servers. It provides a virtual machine (VM) management system that complements Rancher’s capabilities for Kubernetes clusters. By combining Harvester and Rancher, you can effectively manage your on-premises clusters and the infrastructure that hosts them.
• Helm is the standard package manager for Kubernetes applications. It packages an application’s Kubernetes manifests into a collection called a chart, ready to deploy with a single command. Rancher natively supports Helm charts and provides a convenient interface for deploying them into your cluster via its apps system.

By utilizing Rancher alongside other common tools, you can make multicloud Kubernetes even more powerful. Automated storage, local infrastructure management and packaged applications allow you to scale up freely without the hassle of manually provisioning environments and creating your app’s resources.

Deploy to large-scale environments with Rancher Fleet

Rancher also helps you deploy applications using automated GitOps methodologies. Rancher Fleet is a dedicated GitOps solution for containerized workloads that offers transparent visibility, flexible control and support for large-scale deployments to multiple environments.

Rancher Fleet manages your Kubernetes manifests, Helm charts and Kustomize templates for you, converting them into Helm charts that can automatically deploy in your clusters. You can set up Fleet in your Rancher installation by clicking the menu icon in the top-left and then choosing Continuous Delivery from the slide-out main menu:

Click Get started to connect your first Git repository and deploy it to your clusters. Once again, Rancher permits you to use standardized delivery workflows in all your environments. You’re no longer restricted to a single cloud vendor, delivery channel or platform as a service (PaaS):

Conclusion

Multicloud presents new opportunities for more flexible and efficient deployments. Mixing solutions from several different cloud providers lets you select the best option for each of your components while avoiding the risk of vendor lock-in.

Nonetheless, organizations that use multicloud with containers and Kubernetes often experience operational challenges. It’s difficult to manage clusters that exist in several different environments, such as public clouds and on-premises servers. Moreover, implementing centralized monitoring, access control and security policies yourself is highly taxing.

Rancher solves these challenges by providing a single tool for provisioning infrastructure, installing Kubernetes and managing your deployments. It works with Google GKE, Amazon EKS, Azure AKS and your own clusters, making it the ultimate solution for achieving multicloud Kubernetes interoperability. Try Rancher today to provision and scale multicloud Kubernetes.

Companies face multiple challenges when migrating their applications and services to the cloud, and one of them is infrastructure management.

The ideal scenario would be that all workloads could be containerized. In that case, the organization could use a Kubernetes-based service, like Amazon Web Services (AWS), Google Cloud or Azure, to deploy and manage applications, services and storage in a cloud native environment.

Unfortunately, this scenario isn’t always possible. Some legacy applications are either very difficult or very expensive to migrate to a microservices architecture, so running them on virtual machines (VMs) is often the best solution.

Considering the current trend of adopting multicloud and hybrid environments, managing additional infrastructure just for VMs is not optimal. This is where a hyperconverged infrastructure (HCI) can help. Simply put, HCI enables organizations to quickly deploy, manage and scale their workloads by virtualizing all the components that make up the on-premises infrastructure.

That being said, not all HCI solutions are created equal. In this article, you’ll learn more about what an HCI is and then explore Harvester, an enterprise-grade HCI software that offers you unique flexibility and convenience when managing your infrastructure.

What is HCI?

Hyperconverged infrastructure (HCI) is a type of data center infrastructure that virtualizes computing, storage and networking elements in a single system through a hypervisor.

Since virtualized abstractions managed by a hypervisor replaces all physical hardware components (computing, storage and networking), an HCI offers benefits, including the following:

• Easier configuration, deployment and management of workloads.
• Convenience since software-defined data centers (SDDCs) can also be easily deployed.
• Greater scalability with the integration of more nodes to the HCI.
• Tight integration of virtualized components, resulting in fewer inefficiencies and lower total cost of ownership (TCO).

However, the ease of management and the lower TCO of an HCI approach come with some drawbacks, including the following:

• Risk of vendor lock-in when using closed-source HCI platforms.
• Most HCI solutions force all resources to be increased in order to increase any single resource. That is, new nodes add more computing, storage and networking resources to the infrastructure.
• You can’t combine HCI nodes from different vendors, which aggravates the risk of vendor lock-in described previously.

Now that you know what HCI is, it’s time to learn more about Harvester and how it can alleviate the limitations of HCI.

What is Harvester?

According to the Harvester website, “Harvester is a modern hyperconverged infrastructure (HCI) solution built for bare metal servers using enterprise-grade open-source technologies including Kubernetes, KubeVirt and Longhorn.” Harvester is an ideal solution for those seeking a Cloud native HCI offering — one that is both cost-effective and able to place VM workloads on the edge, driving IoT integration into cloud infrastructure.

Because Harvester is open source, this automatically means you don’t have to worry about vendor lock-in. Furthermore, since it’s built on top of Kubernetes, Harvester offers incredible scalability, flexibility and reliability.

Additionally, Harvester provides a comprehensive set of features and capabilities that make it the ideal solution for deploying and managing enterprise applications and services. Among these characteristics, the following stand out:

• Built on top of Kubernetes.
• Full VM lifecycle management, thanks to KubeVirt.
• Support for VM cloud-init templates.
• VM live migration support.
• VM backup, snapshot and restore capabilities.
• Distributed block storage and storage tiering, thanks to Longhorn.
• Powerful monitoring and logging since Harvester uses Grafana and Prometheus as its observability backend.
• Seamless integration with Rancher, facilitating multicluster deployments as well as deploying and managing VMs and Kubernetes workloads from a centralized dashboard.

Now that you know about some of Harvester’s basic features, let’s take a more in-depth look at some of the more prominent features.

How Rancher and Harvester can help with Kubernetes deployments on HCI

Managing multicluster and hybrid-cloud environments can be intimidating when you consider how complex it can be to monitor infrastructure, manage user permissions and avoid vendor lock-in, just to name a few challenges. In the following sections, you’ll see how Harvester, or more specifically, the synergy between Harvester and Rancher, can make life easier for ITOps and DevOps teams.

Straightforward installation

There is no one-size-fits-all approach to deploying an HCI solution. Some vendors sacrifice features in favor of ease of installation, while others require a complex installation process that includes setting up each HCI layer separately.

However, with Harvester, this is not the case. From the beginning, Harvester was built with ease of installation in mind without making any compromises in terms of scalability, reliability, features or manageability.

To do this, Harvester treats each node as an HCI appliance. This means that when you install Harvester on a bare-metal server, behind the scenes, what actually happens is that a simplified version of SLE Linux is installed, on top of which Kubernetes, KubeVirt, Longhorn, Multus and the other components that make up Harvester are installed and configured with minimal effort on your part. In fact, the manual installation process is no different from that of a modern Linux distribution, save for a few notable exceptions:

• Installation mode: Early on in the installation process, you will need to choose between creating a new cluster (in which case the current node becomes the management node) or joining an existing Harvester cluster. This makes sense since you’re actually setting up a Kubernetes cluster.
• Virtual IP: During the installation, you will also need to set an IP address from which you can access the main node of the cluster (or join other nodes to the cluster).
• Cluster token: Finally, you should choose a cluster token that will be used to add new nodes to the cluster.

When it comes to installation media, you have two options for deploying Harvester:

• ISO installation: This consists of downloading the latest Harvester ISO image and then installing it manually on a bare-metal server. The documentation includes a complete walkthrough of the Harvester installation that includes a video showing the step-by-step process.
• PXE boot installation: This is the ideal option to install Harvester automatically using iPXE boot scripts. To learn more about the PXE installation procedure and prerequisites, check out this documentation.

It should be noted that, regardless of the deployment method, you can use a Harvester configuration file to provide various settings. This makes it even easier to automate the installation process and enforce the infrastructure as code (IaC) philosophy, which you’ll learn more about later on.

For your reference, the following is what a typical configuration file looks like (taken from the official documentation):

scheme_version: 1
server_url: https://cluster-VIP:443
token: TOKEN_VALUE
os:
  ssh_authorized_keys:
    - ssh-rsa AAAAB3NzaC1yc2EAAAADAQAB...
    - github:username
  write_files:
  - encoding: ""
    content: test content
    owner: root
    path: /etc/test.txt
    permissions: '0755'
  hostname: myhost
  modules:
    - kvm
    - nvme
  sysctls:
    kernel.printk: "4 4 1 7"
    kernel.kptr_restrict: "1"
  dns_nameservers:
    - 8.8.8.8
    - 1.1.1.1
  ntp_servers:
    - 0.suse.pool.ntp.org
    - 1.suse.pool.ntp.org
  password: rancher
  environment:
    http_proxy: http://myserver
    https_proxy: http://myserver
  labels:
    topology.kubernetes.io/zone: zone1
    foo: bar
    mylabel: myvalue
install:
  mode: create
  management_interface:
    interfaces:
    - name: ens5
      hwAddr: "B8:CA:3A:6A:64:7C"
    method: dhcp
  force_efi: true
  device: /dev/vda
  silent: true
  iso_url: http://myserver/test.iso
  poweroff: true
  no_format: true
  debug: true
  tty: ttyS0
  vip: 10.10.0.19
  vip_hw_addr: 52:54:00:ec:0e:0b
  vip_mode: dhcp
  force_mbr: false
system_settings:
  auto-disk-provision-paths: ""

All in all, Harvester offers a straightforward installation on bare-metal servers. What’s more, out of the box, Harvester offers powerful capabilities, including a convenient host management dashboard (more on that later).

Host management

Nodes, or hosts, as they are called in Harvester, are the heart of any HCI infrastructure. As discussed, each host provides the computing, storage and networking resources used by the HCI cluster. In this sense, Harvester provides a modern UI that gives your team a quick overview of each host’s status, name, IP address, CPU usage, memory, disks and more. Additionally, your team can perform all kinds of routine operations intuitively just by right-clicking on each host’s hamburger menu:

• Node maintenance: This is handy when your team needs to remove a node from the cluster for a long time for maintenance or replacement. Once the node enters the maintenance node, all VMs are automatically distributed across the rest of the active nodes. This eliminates the need to live migrate VMs separately.
• Cordoning a node: When you cordon a node, it’s marked as “unschedulable,” which is useful for quick tasks like reboots and OS upgrades.
• Deleting a node: This permanently removes the node from the cluster.
• Multi-disk management: This allows adding additional disks to a node as well as assigning storage tags. The latter is useful to allow only certain nodes or disks to be used for storing Longhorn volume data.
• KSMtuned mode management: In addition to the features described earlier, Harvester allows your team to tune the use of kernel same-page merging (KSM) as it deploys the KSM Tuning Service ksmtuned on each node as a DaemonSet.

To learn more on how to manage the run strategy and threshold coefficient of ksmtuned, as well as more details on the other host management features described, check out this documentation.

As you can see, managing nodes through the Harvester UI is really simple. However, your ops team will spend most of their time managing VMs, which you’ll learn more about next.

VM management

Harvester was designed with great emphasis on simplifying the management of VMs’ lifecycles. Thanks to this, IT teams can save valuable time when deploying, accessing and monitoring VMs. Following are some of the main features that your team can access from the Harvester Virtual Machines page.

Harvester basic VM management features

As you would expect, the Harvester UI facilitates basic operations, such as creating a VM (including creating Windows VMs), editing VMs and accessing VMs. It’s worth noting that in addition to the usual configuration parameters, such as VM name, disks, networks, CPU and memory, Harvester introduces the concept of the namespace. As you might guess, this additional level of abstraction is made possible by Harvester running on top of Kubernetes. In practical terms, this allows your Ops team to create isolated virtual environments (for example, development and production), which facilitate resource management and security.

Furthermore, Harvester also supports injecting custom cloud-init startup scripts into a VM, which speeds up the deployment of multiple VMs.

Harvester advanced VM management features

Today, any virtualization tool allows the basic management of VMs. In that sense, where enterprise-grade platforms like Harvester stand out from the rest is in their advanced features. These include performing VM backup, snapshot and restore; doing VM live migration; adding hot-plug volumes to running VMs; cloning VMs with volume data; and overcommitting CPU, memory and storage.

While all these features are important, Harvester’s ability to ensure the high availability (HA) of VMs is hands down the most crucial to any modern data center. This feature is available on Harvester clusters with three or more nodes and allows your team to migrate live VMs from one node to another when necessary.

Furthermore, not only is live VM migration useful for maintaining HA, but it is also a handy feature when performing node maintenance when a hardware failure occurs or your team detects a performance drop on one or more nodes. Regarding the latter, performance monitoring, Harvester provides out-of-the-box integration with Grafana and Prometheus.

Built-in monitoring

Prometheus and Grafana are two of the most popular open source observability tools today. They’re highly customizable, powerful and easy to use, making them ideal for monitoring key VMs and host metrics.

Grafana is a data-focused visualization tool that makes it easy to monitor your VM’s performance and health. It can provide near real-time performance metrics, such as CPU and memory usage and disk I/O. It also offers comprehensive dashboards and alerts that are highly configurable. This allows you to customize Grafana to your specific needs and create useful visualizations that can help you quickly identify issues.

Meanwhile, Prometheus is a monitoring and alerting toolkit designed for large-scale, distributed systems. It collects time series data from your VMs and hosts, allowing you to quickly and accurately track different performance metrics. Prometheus also provides alerts when certain conditions have been met, such as when a VM is running low on memory or disk space.

All in all, using Grafana and Prometheus together provide your team with comprehensive observability capabilities by means of detailed graphs and dashboards that can help them to identify why an issue is occurring. This can help you take corrective action more quickly and reduce the impact of any potential issues.

Infrastructure as Code

Infrastructure as code (IaC) has become increasingly important in many organizations because it allows for the automation of IT infrastructure, making it easier to manage and scale. By defining IT infrastructure as code, organizations can manage their VMs, disks and networks more efficiently while also making sure that their infrastructure remains in compliance with the organization’s policies.

With Harvester, users can define their VMs, disks and networks in YAML format, making it easier to manage and version control virtual infrastructure. Furthermore, thanks to the Harvester Terraform provider, DevOps teams can also deploy entire HCI clusters from scratch using IaC best practices.

This allows users to define the infrastructure declaratively, allowing operations teams to work with developer tools and methodologies, helping them become more agile and effective. In turn, this saves time and cost and also enables DevOps teams to deploy new environments or make changes to existing ones more efficiently.

Finally, since Harvester enforces IaC principles, organizations can make sure that their infrastructure remains compliant with security, regulatory and governance policies.

Rancher integration

Up to this point, you’ve learned about key aspects of Harvester, such as its ease of installation, its intuitive UI, its powerful built-in monitoring capabilities and its convenient automation, thanks to IaC support. However, the feature that takes Harvester to the next level is its integration with Rancher, the leading container management tool.

Harvester integration with Rancher allows DevOps teams to manage VMs and Kubernetes workloads from a single control panel. Simply put, Rancher integration enables your organization to combine conventional and Cloud native infrastructure use cases, making it easier to deploy and manage multi-cloud and hybrid environments.

Furthermore, Harvester’s tight integration with Rancher allows your organization to streamline user and system management, allowing for more efficient infrastructure operations. Additionally, user access control can be centralized in order to ensure that the system and its components are protected.

Rancher integration also allows for faster deployment times for applications and services, as well as more efficient monitoring and logging of system activities from a single control plane. This allows DevOps teams to quickly identify and address issues related to system performance, as well as easily detect any security risks.

Overall, Harvester integration with Rancher provides DevOps teams with a comprehensive, centralized system for managing both VMs and containerized workloads. In addition, this approach provides teams with improved convenience, observability and security, making it an ideal solution for DevOps teams looking to optimize their infrastructure operations.

Conclusion

One of the biggest challenges facing companies today is migrating their applications and services to the cloud. In this article, you’ve learned how you can manage Kubernetes and VM-based environments with the aid of Harvester and Rancher, thus facilitating your application modernization journey from monolithic apps to microservices.

Both Rancher and Harvester are part of the rich SUSE ecosystem that helps your business deploy multi-cloud and hybrid-cloud environments easily across any infrastructure. Harvester is an open source HCI solution. Try it for free today.