Tuning performance

Default worker node settings

By default, your worker nodes have the operating system and compute hardware of the worker node flavor that you choose when you create the worker pool.

Modifying worker node kernel settings to optimize performance

Cluster worker nodes are configured for a level of stability, optimization, and performance that is expected to meet the needs of most workloads. Usually, it is not recommended to change your worker node kernel settings, as such changes can create unusual and unintended issues. However, if your workload has highly unique performance optimization requirements that necessitate changes to your kernel settings, a custom Kubernetes daemonset can be applied to change the kernel configuration. Understand that these changes can have significant negative consequences and that you implement changes to the kernel settings configuration at your own risk.

If you change the configuration of your kernel settings, make sure you document and save the exact changes that you make. If you open a support ticket for any issues related to the cluster, you must specify these changes. These configuration changes might be responsible for the issue, and you might be asked to revert the changes as part of the issue investigation. In this case, you are responsible for reverting any kernel configuration changes you implement.

Changing the default kernel settings can have negative effects on your cluster. Make these changes at your own risk.

You can change the default kernel settings by applying a custom Kubernetes DaemonSet with an init Container to your cluster. The daemon set modifies the settings for all existing worker nodes and applies the settings to any new worker nodes that are provisioned in the cluster. The init container makes sure that these modifications occur before other pods are scheduled on the worker node. No pods are affected.

You must have the Manager IBM Cloud IAM service access role for all namespaces to run the sample privileged initContainer. After the containers for the deployments are initialized, the privileges are dropped.

Before you begin: Log in to your account. If applicable, target the appropriate resource group. Set the context for your cluster.

1. Save the following daemon set in a file named worker-node-kernel-settings.yaml. In the spec.template.spec.initContainers section, add the fields and values for the sysctl parameters that you want to tune. This example daemon set changes the default maximum number of connections that are allowed in the environment via the net.core.somaxconn setting and the ephemeral port range via the net.ipv4.ip_local_port_range setting.

   apiVersion: apps/v1
   kind: DaemonSet
   metadata:
     name: kernel-optimization
     namespace: kube-system
     labels:
       tier: management
       app: kernel-optimization
   spec:
     selector:
       matchLabels:
         name: kernel-optimization
     template:
       metadata:
         labels:
           name: kernel-optimization
       spec:
         hostNetwork: true
         hostPID: true
         hostIPC: true
         initContainers:
           - command:
               - sh
               - -c
               - sysctl -w net.ipv4.tcp_syn_retries="5"; sysctl -w net.ipv4.tcp_fin_timeout="15";
             image: us.icr.io/armada-master/network-alpine:latest
             imagePullPolicy: Always 
             name: sysctl
             resources: {}
             securityContext:
               privileged: true
               capabilities:
                 add:
                   - NET_ADMIN
             volumeMounts:
               - name: modifysys
                 mountPath: /sys
         containers:
           - resources:
               requests:
                 cpu: 0.01
             image: us.icr.io/armada-master/network-alpine:latest
             name: sleepforever
             command: ["/bin/sh", "-c"]
             args:
               - >
                 while true; do
                   sleep 100000;
                 done
         volumes:
           - name: modifysys
             hostPath:
               path: /sys
   

2. Apply the daemon set to your worker nodes. The changes are applied immediately.

   kubectl apply -f worker-node-kernel-settings.yaml
   

To revert your worker nodes sysctl parameters to the default values, follow these steps.

1. Delete the daemon set. The initContainers that applied the custom settings are removed.

   kubectl delete ds kernel-optimization
   

2. Reboot all worker nodes in the cluster. The worker nodes come back online with the default values applied.

Optimizing pod performance

If you have specific performance workload demands, you can change the default settings for the Linux kernel sysctl parameters on pod network namespaces.

To optimize kernel settings for app pods, you can insert an initContainer patch into the pod/ds/rs/deployment YAML for each deployment. The initContainer is added to each app deployment that is in the pod network namespace for which you want to optimize performance.

Before you begin, ensure you have the Manager IBM Cloud IAM service access role for all namespaces to run the sample privileged initContainer. After the containers for the deployments are initialized, the privileges are dropped.

1. Save the following initContainer patch in a file named pod-patch.yaml and add the fields and values for the sysctl parameters that you want to tune. This example initContainer changes the default maximum number of connections allowed in the environment via the net.core.somaxconn setting and the ephemeral port range via the net.ipv4.ip_local_port_range setting.

   spec:
     template:
       spec:
         initContainers:
         - command:
           - sh
           - -c
           - sysctl -e -w net.core.somaxconn=32768;  sysctl -e -w net.ipv4.ip_local_port_range="1025 65535";
           image: alpine:3.6
           imagePullPolicy: IfNotPresent
           name: sysctl
           resources: {}
           securityContext:
             privileged: true
   

2. Patch each of your deployments.

   kubectl patch deployment <deployment_name> --patch pod-patch.yaml
   

3. If you changed the net.core.somaxconn value in the kernel settings, most apps can automatically use the updated value. However, some apps might require you to manually change the corresponding value in your app code to match the kernel value. For example, if you're tuning the performance of a pod where an NGINX app runs, you must change the value of the backlog field in the NGINX app code to match. For more information, see this NGINX blog post.

Optimizing network keepalive sysctl settings

If a pod has long running TCP connections that are occasionally disconnected when they are idle for a period of time, it might help to change the sysctl keepalive settings for the pod.

These scenarios and suggested settings are also described in the Troubleshooting Outgoing Connection Issues with IBM VPC Public and Service Gateways blog.

There currently isn't a way to set these sysctl keepalive settings on all pods by default in a cluster. The best way to modify the settings on all pods is to use a privileged initContainer. Review the following example of how to set up an initContainer for a deployment in a test-ns namespace.

Deploy the following example initContainer. Remember to change the containers: section to your own application containers. The initContainer then sets the sysctl settings for all the regular containers in the pod because they all share the same network namespace.

```sh {: pre}
kubectl apply -f - << EOF
apiVersion: apps/v1
kind: Deployment
metadata:
  name: test-sysctl
  namespace: test-ns
  labels:
    run: test-sysctl
spec:
  replicas: 2
  selector:
    matchLabels:
      run: test-sysctl
  template:
    metadata:
      labels:
        run: test-sysctl
    spec:
      initContainers:
      - command:
        - sh
        - -c
        - sysctl -e -w net.ipv4.tcp_keepalive_time=40; sysctl -e -w net.ipv4.tcp_keepalive_intvl=15; sysctl -e -w net.ipv4.tcp_keepalive_probes=6;
        image: us.icr.io/armada-master/alpine:latest
        imagePullPolicy: IfNotPresent
        name: sysctl-init
        resources: {}
        securityContext:
          privileged: true
      containers:
      - name: test-sysctl
        image: us.icr.io/armada-master/alpine:latest
        command: ["sleep", "2592000"]
  EOF
```      

Adjusting cluster metrics provider resources

Your cluster has a metrics service provided by the metrics-server deployment in the kube-system namespace. The metrics-server resource requests are based on the number of nodes in the cluster and are optimized for clusters with 30 or less pods per worker node. The metric service matches the memory and CPU limits of the resource requests.

The metrics-service containers can be "out-of-memory killed" if the memory requests are too low. They might respond very slowly or fail liveness and readiness probes, due to CPU throttling if the CPU requests are too low.

Memory use is driven by the number of pods in the cluster. CPU use is driven by the number of requests for metrics (HPAs, kubectl top nodes / pods, and so on) and by API discovery requests. The metrics-server provides a Kubernetes API, so that clients such as kubectl that use API discovery place some load on the metrics-server even if they don't use metrics.

The following symptoms might indicate a need to adjust the metrics-server resources:

- The `metrics-server` is restarting frequently.

- Deleting a namespace results in the namespace that is stuck in a `Terminating` state and `kubectl describe namespace` includes a condition reporting a metrics API discovery error.

- `kubectl top pods`, `kubectl top nodes`, other `kubectl` commands, or applications that use the Kubernetes API to log Kubernetes errors such as:

    ```sh {: screen}
    The server is currently unable to handle the request (get pods.metrics.k8s.io)
    ```        
    ```sh {: screen}
    Discovery failed for some groups, 1 failing: unable to retrieve the complete list of server APIs: metrics.k8s.io/v1beta1: the server is currently unable to handle the request
    ```
- HorizontalPodAutoscalers (HPAs) do not scale deployments.

- Running `kubectl get apiservices v1beta1.metrics.k8s.io` results in a status like:

    ```sh {: screen}
    NAME                     SERVICE                      AVAILABLE                      AGE
    v1beta1.metrics.k8s.io   kube-system/metrics-server   False (FailedDiscoveryCheck)   139d
    ```    

- `baseCPU`
- `cpuPerNode`
- `baseMemory`
- `memoryPerNode`

Where:
```sh
cpuRequest = baseCPU + cpuPerNode * number_of_nodes
memoryRequest = baseMemory + memoryPerNode * number_of_nodes
```    
{: pre}

apiVersion: v1
kind: ConfigMap
metadata:
  labels:
    addonmanager.kubernetes.io/mode: EnsureExists
    kubernetes.io/cluster-service: "true"
  name: metrics-server-config
  namespace: kube-system
data:
  NannyConfiguration: |-
    apiVersion: nannyconfig/v1alpha1
    kind: NannyConfiguration

apiVersion: v1
kind: ConfigMap
metadata:
  labels:
    addonmanager.kubernetes.io/mode: EnsureExists
    kubernetes.io/cluster-service: "true"
  name: metrics-server-config
  namespace: kube-system
data:
  NannyConfiguration: |-
    apiVersion: nannyconfig/v1alpha1
    kind: NannyConfiguration
    baseCPU: 100m
    cpuPerNode: 1m
    baseMemory: 40Mi
    memoryPerNode: 6Mi

baseCPU: 100m
cpuPerNode: 1m
baseMemory: 40Mi
memoryPerNode: 6Mi

kubectl edit cm metrics-server-config -n kube-system

kubectl delete cm metrics-server-config -n kube-system

kubectl get pod -n kube-system -l k8s-app=metrics-server
NAME                             READY   STATUS    RESTARTS   AGE
metrics-server-9fb4947d6-s6sgl   3/3     Running   0          2d4h

kubectl describe pod -n kube-system metrics-server-9fb4947d6-s6sgl

Containers:
  metrics-server:
    Container ID:  containerd://fe3d07c9a2541242d36da8097de3896f740c1363f6d2bfd01b8d96a641192b1b
    Image:         registry.ng.bluemix.net/armada-master/metrics-server:v0.4.4
    Image ID:      registry.ng.bluemix.net/armada-master/metrics-server@sha256:c2c63900d0e080c2413b5f35c5a59b5ed3b809099355728cf47527aa3f35477c
    Port:          4443/TCP
    Host Port:     0/TCP
    Command:
      /metrics-server
      --metric-resolution=45s
      --secure-port=4443
      --tls-cert-file=/etc/metrics-server-certs/tls.crt
      --tls-private-key-file=/etc/metrics-server-certs/tls.key
    State:          Running
      Started:      Fri, 10 Sep 2021 17:31:39 +0000
    Last State:     Terminated
      Reason:       OOMKilled
      Exit Code:    137
      Started:      Fri, 10 Sep 2021 05:59:51 +0000
      Finished:     Fri, 10 Sep 2021 17:31:37 +0000
    Ready:          True
    Restart Count:  36

Last State:     Terminated
  Reason:       OOMKilled
  Exit Code:    137

Last State:     Terminated
  Reason: Error
  Exit Code: 137

Events:
  Warning Unhealthy 46m (x5 over 80m) kubelet Liveness probe failed: Get "https://198.18.68.236:4443/livez": context deadline exceeded (Client.Timeout exceeded while awaiting headers)
  Warning Unhealthy 26m (x65 over 89m) kubelet Liveness probe failed: Get "https://198.18.68.236:4443/livez": net/http: TLS handshake timeout
  Warning Unhealthy 21m (x10 over 76m) kubelet Readiness probe failed: Get "https://198.18.68.236:4443/readyz": net/http: request canceled (Client.Timeout exceeded while awaiting headers)
  Warning Unhealthy 115s (x93 over 90m) kubelet Readiness probe failed: Get "https://198.18.68.236:4443/readyz": net/http: TLS handshake timeout

Enabling huge pages

Classic infrastructure Virtual Private Cloud

You can enable the Kubernetes HugePages scheduling in clusters that run Kubernetes version 1.19 or later. The only supported page size is 2 MB per page, which is the default size of the Kubernetes feature gate.

Huge pages scheduling is a beta feature in IBM Cloud Kubernetes Service and is subject to change.

By default, the CPU of your worker nodes allocates RAM in chunks, or pages, of 4 KB. When your app requires more RAM, the system must continue to look up more pages, which can slow down processing. With huge pages, you can increase the page size to 2 MB to increase performance for your RAM-intensive apps like databases for artificial intelligence (AI), internet of things (IoT), or machine learning workloads. For more information about huge pages, see the Linux kernel documentation.

You can reboot the worker node and the huge pages configuration persists. However, the huge pages configuration does not persist across any other worker node life cycle operations. You must repeat the enablement steps each time that you update, reload, replace, or add worker nodes.

• Operator platform access role and Manager service access role for the cluster in IBM Cloud IAM

Before you begin: Log in to your account. If applicable, target the appropriate resource group. Set the context for your cluster.

1. Create a hugepages-ds.yaml configuration file to enable huge pages. The following sample YAML uses a daemon set to run the pod on every worker node in your cluster. You can set the allocation of huge pages that are available on the worker node by using the vm.nr_hugepages parameter. This example allocates 512 pages at 2 MB per page, for 1 GB of total RAM allocated exclusively for huge pages.

   Want to enable huge pages only for certain worker nodes, such as a worker pool that you use for RAM-intensive apps? Label and taint your worker pool, and then add affinity rules to the daemon set so that the pods are deployed only to the worker nodes in the worker pool that you specify.

   apiVersion: apps/v1
   kind: DaemonSet
   metadata:
     name: hugepages-enablement
     namespace: kube-system
     labels:
       tier: management
       app: hugepages-enablement
   spec:
     selector:
       matchLabels:
         name: hugepages-enablement
     template:
       metadata:
         labels:
           name: hugepages-enablement
       spec:
         hostPID: true
         initContainers:
           - command:
               - sh
               - -c
               # Customize allocated Hugepages by providing the value
               - "echo vm.nr_hugepages=512 > /etc/sysctl.d/90-hugepages.conf"
             image: alpine:3.6
             imagePullPolicy: IfNotPresent
             name: sysctl
             resources: {}
             securityContext:
               privileged: true
             volumeMounts:
               - name: modify-sysctld
                 mountPath: /etc/sysctl.d
         containers:
           - resources:
               requests:
                 cpu: 0.01
             image: alpine:3.6
             # once the init container completes, keep the pod running for worker node changes
             name: sleepforever
             command: ["/bin/sh", "-c"]
             args:
               - >
                 while true; do
                     sleep 100000;
                 done
         volumes:
           - name: modify-sysctld
             hostPath:
               path: /etc/sysctl.d
   

2. Apply the file that you previously created.

   kubectl apply -f hugepages-ds.yaml
   

3. Verify that the pods are Running.

   kubectl get pods
   

4. Restart the kubelet that runs on each worker node by rebooting the worker nodes. Do not reload the worker node to restart the kubelet. Reloading the worker node before the kubelet picks up on the huge pages enablement causes the enablement to fail.

   1. List the worker nodes in your cluster.

      ibmcloud ks worker ls -c <cluster_name_or_ID>
      

   2. Reboot the worker nodes. You can reboot multiple worker nodes by including multiple -w options, but make sure to leave enough worker nodes running at the same time for your apps to avoid an outage.

      ibmcloud ks worker reboot -c <cluster_name_or_ID> -w <worker1_ID> -w <worker2_ID>
      

5. Create a hugepages-test.yaml test pod that mounts huge pages as a volume and uses resource limits and requests to set how much of the huge pages resources that the pod uses. Note: If you used labels, taints, and affinity rules to enable huge pages on select worker nodes only, include these same rules in your test pod.

   apiVersion: v1
   kind: Pod
   metadata:
     name: hugepages-example
   spec:
     containers:
     - name: hugepages-example
       image: fedora:34
       command:
       - sleep
       - inf
       volumeMounts:
       - mountPath: /hugepages-2Mi
         name: hugepage-2mi
       resources:
         limits:
           hugepages-2Mi: 100Mi
           memory: 100Mi
         requests:
           memory: 100Mi
     volumes:
     - name: hugepage-2mi
       emptyDir:
         medium: HugePages-2Mi
   

6. Apply the pod file that you previously created.

   kubectl apply -f hugepages-pod.yaml
   

7. Verify that your pod uses the huge pages resources.

   1. Check that your pod is Running. The pod does not run if no worker nodes with huge pages are available.

      kubectl get pods
      

   2. Log in to the pod.

      kubectl exec -it <pod> /bin/sh
      

   3. Verify that your pod can view the sizes of the huge pages.

      ls /sys/kernel/mm/hugepages
      

      Example output

      hugepages-1048576kB  hugepages-2048kB
      

8. Optional: Remove the enablement daemon set. Keep in mind that you must re-create the daemon set if you need to update, reload, replace, or add worker nodes with huge pages later.

   kubectl -n kube-system delete daemonset hugepages-enablement
   

9. Repeat these steps whenever you update, reload, replace, or add worker nodes.

To troubleshoot worker nodes with huge pages, you can only reboot the worker node. The huge pages configuration does not persist across any other worker node life cycle operation, such as updating, reloading, replacing, or adding worker nodes. To remove the huge pages configuration from your cluster, you can update, reload, or replace all the worker nodes.

Changing the Calico maximum transmission unit (MTU)

Increase or decrease the Calico plug-in maximum transmission unit (MTU) to meet the network throughput requirements of your environment.

All VPC workers nodes support jumbo frames. However, on classic infrastructure, only bare metal workers support jumbo frames.

By default, the Calico network plug-in in your IBM Cloud Kubernetes Service cluster has an MTU of 1480 bytes. For most cases, this default MTU value provides sufficient throughput for packets that are sent and received in your network workloads. Review the following cases in which you might need to modify the default Calico MTU:

• Jumbo frames have an MTU value in the range of 1500 to 9000. To ensure that your cluster's pod network can use this higher MTU value, you can increase the Calico MTU to 20 bytes less than the jumbo frame MTU. This 20 byte difference allows space for packet header on encapsulated packets. For example, if your worker nodes' jumbo frames are set to 9000, you can set the Calico MTU to 8980. Note that all worker nodes in the cluster must use the same Calico MTU, so to increase the Calico MTU, all worker nodes in the cluster must be bare metal and use jumbo frames.
• If you have a VPN connection set up for your cluster, some VPN connections require a smaller Calico MTU than the default. Check with the VPN service provider to determine whether a smaller Calico MTU is required.
• If your cluster's worker nodes exist on different subnets, increasing the MTU value for the worker nodes and for the Calico MTU can allow pods to use the full bandwidth capability of the worker nodes.

Before you begin

If your worker nodes still run the default MTU value, increase the MTU value for your worker nodes first before you increase the MTU value for the Calico plug-in. For example, you can apply the following daemon set to change the MTU for your worker nodes jumbo frames to 9000 bytes. Note the interface names that are used in the ip link command vary depending on the type of your worker nodes.

• Example command for Bare Metal worker nodes: ip link set dev bond0 mtu 9000;ip link set dev bond1 mtu 9000;
• Example command VPC Gen 2 worker nodes: ip link set dev ens3 mtu 9000;

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: jumbo-apply
  namespace: kube-system
  labels:
    tier: management
    app: jumbo-apply
spec:
  selector:
    matchLabels:
      name: jumbo-apply
  template:
    metadata:
      labels:
        name: jumbo-apply
    spec:
      hostNetwork: true
      hostPID: true
      hostIPC: true
      tolerations:
      - operator: Exists
      initContainers:
        - command:
            - sh
            - -c
            - ip link set dev bond0 mtu 9000;ip link set dev bond1 mtu 9000; # Update this command based on your worker node type.
          image: alpine:3.6
          imagePullPolicy: IfNotPresent
          name: iplink
          resources: {}
          securityContext:
            privileged: true
            capabilities:
              add:
                - NET_ADMIN
          volumeMounts:
            - name: modifysys
              mountPath: /sys
      containers:
        - resources:
            requests:
              cpu: 0.01
          image: alpine:3.6
          name: sleepforever
          command: ["/bin/sh", "-c"]
          args:
            - >
              while true; do
                sleep 100000;
              done
      volumes:
        - name: modifysys
          hostPath:
             path: /sys

Disabling the port map plug-in

The portmap plug-in for the Calico container network interface (CNI) enables you to use a hostPort to expose your app pods on a specific port on the worker node. Prevent iptables performance issues by removing the port map plug-in from your cluster's Calico CNI configuration.

When you have many services in your cluster, such as more than 500 services, or many ports on services, such as more than 50 ports per service for 10 or more services, many iptables rules are generated for the Calico and Kubernetes network policies for these services. Using many iptables rules can lead to performance issues for the port map plug-in, and might prevent future updates of iptables rules or cause the calico-node container to restart when no lock is received to make iptables rules updates within a specified time. To prevent these performance issues, you can disable the port map plug-in by removing it from your cluster's Calico CNI configuration.

If you must use hostPorts, don't disable the port map plug-in.